arXiv subjects

Xiaohong Li

Publications and source records attributed to Xiaohong Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

VeriFuzzy: A Dynamic Verifiable Fuzzy Search Service for Encrypted Cloud Data

Enabling search over encrypted cloud data is essential for privacy-preserving data outsourcing. While searchable encryption has evolved to support individual requirements like fuzzy matching, dynamic updates, and result verification, designing a service that supports dynamic, verifiable fuzzy search (DVFS) over encrypted cloud data remains a fundamental challenge due to inherent conflicts between underlying technologies. Existing approaches struggle with simultaneously achieving efficiency, functionality, and security, often forcing impractical trade-offs. This paper presents \textbf{VeriFuzzy}, a novel DVFS service framework that cohesively integrates three innovations: an \textit{Enhanced Virtual Binary Tree (EVBTree)} that decouples fuzzy semantics from index logic to support $O(\log n)$ search/updates; a \textit{blockchain-reconstructed verification} mechanism that ensures result integrity with logarithmic complexity; and a \textit{dual-repository state management} scheme that achieves IND-CKA2 security by neutralizing branch leakage. Extensive evaluation on 3,500+ documents shows VeriFuzzy achieves 41\% faster search, $5\times$ more efficient verification, and constant-time index updates compared to state-of-the-art alternatives. Our code and dataset are now open source, hoping to inspire future DVFS research.

cs.CR↗

arXiv2025

3D structures of the base of small-scale recurrent jets revealed by Solar orbiter

Solar jets, characterized by small-scale plasma ejections along open magnetic field lines or the legs of large-scale coronal loops, play a crucial role in the dynamics of the solar atmosphere. Although spectral and EUV images have been widely used to analyze the formation and evolution of jets, the detailed 3D structure at the base of the jet has not been studied in detail due to the limitations in the spatial resolution of observations. Solar Orbiter enables us to investigate the structure of solar jets with much higher spatial and temporal resolutions and from a different angle than from Earth. By combining observations made by instruments onboard Solar Orbiter with data from the SDO, we analyzed recurrent solar jets originating in a mixed-polarity region near an active region. Additionally, we employed potential field and magnetohydrostatic extrapolation techniques to determine the magnetic field topology associated with the jets. The jets display dynamic, multi-strand outflows emanating from compact bright kernels above the magnetic inversion line, with apparent speeds exceeding 100 km/s. Magnetic field evolution reveals continuous flux cancellation at the jet footpoints. Throughout the sequence, base flows are confined within quasi-separatrix layers, with the highest velocities and temperatures located near coronal null points. Over four eruptions, the magnetic topology evolves from a simple fan-spine configuration with a single null to a more complex dome-shaped base containing multiple nulls with separatrix curtain, accompanied by a morphological transition from narrow, well-collimated spire to broader, fragmented outflows. These results provide the first direct observational evidence that dynamic changes in null-point geometry modulate jet morphology and energetics via successive reconnection episodes.

astro-ph.SR↗

arXiv2025

Think Like an Engineer: A Neuro-Symbolic Collaboration Agent for Generative Software Requirements Elicitation and Self-Review

The vision of End-User Software Engineering (EUSE) is to empower non-professional users with full control over the software development lifecycle. It aims to enable users to drive generative software development using only natural language requirements. However, since end-users often lack knowledge of software engineering, their requirement descriptions are frequently ambiguous, raising significant challenges to generative software development. Although existing approaches utilize structured languages like Gherkin to clarify user narratives, they still struggle to express the causal logic between preconditions and behavior actions. This paper introduces RequireCEG, a requirement elicitation and self-review agent that embeds causal-effect graphs (CEGs) in a neuro-symbolic collaboration architecture. RequireCEG first uses a feature tree to analyze user narratives hierarchically, clearly defining the scope of software components and their system behavior requirements. Next, it constructs the self-healing CEGs based on the elicited requirements, capturing the causal relationships between atomic preconditions and behavioral actions. Finally, the constructed CEGs are used to review and optimize Gherkin scenarios, ensuring consistency between the generated Gherkin requirements and the system behavior requirements elicited from user narratives. To evaluate our method, we created the RGPair benchmark dataset and conducted extensive experiments. It achieves an 87% coverage rate and raises diversity by 51.88%.

cs.SE↗

arXiv2025

It Only Gets Worse: Revisiting DL-Based Vulnerability Detectors from a Practical Perspective

With the growing threat of software vulnerabilities, deep learning (DL)-based detectors have gained popularity for vulnerability detection. However, doubts remain regarding their consistency within declared CWE ranges, real-world effectiveness, and applicability across scenarios. These issues may lead to unreliable detection, high false positives/negatives, and poor adaptability to emerging vulnerabilities. A comprehensive analysis is needed to uncover critical factors affecting detection and guide improvements in model design and deployment. In this paper, we present VulTegra, a novel evaluation framework that conducts a multidimensional comparison of scratch-trained and pre-trained-based DL models for vulnerability detection. VulTegra reveals that state-of-the-art (SOTA) detectors still suffer from low consistency, limited real-world capabilities, and scalability challenges. Contrary to common belief, pre-trained models are not consistently better than scratch-trained models but exhibit distinct strengths in specific contexts.Importantly, our study exposes the limitations of relying solely on CWE-based classification and identifies key factors that significantly affect model performance. Experimental results show that adjusting just one such factor consistently improves recall across all seven evaluated detectors, with six also achieving better F1 scores. Our findings provide deeper insights into model behavior and emphasize the need to consider both vulnerability types and inherent code features for effective detection.

cs.SE↗

arXiv2025

Response of the solar atmosphere to flux emergence: With emergence-driven prominence formation

Flux emergence is crucial for the formation of solar active regions and triggering of various eruptions. However, the detailed mechanisms by which flux emergence drives these eruptions remain unclear and require numerical investigation. Using 2.5-dimensional magnetohydrodynamic simulations, we investigate the interaction between emerging flux and background magnetic fields and dynamics of the induced eruptions. By systematically varying the strength and angle of the emerging magnetic field relative to the background field, we investigate its impact on the initiation and evolution of solar eruptions. The simulations show that magnetic reconnection between the emerging flux and background field drives the formation of current sheets, magnetic islands and multithermal jets. Stronger magnetic fields result in earlier eruptions, more energetic jets, and enhanced heating. The formation and ejection of magnetic islands affect the structure and dynamics of the jet. When the hot and cool components of jets reach the other footpoint of magnetic loops, they will generate spicules near the transition region. Varying the angle between the emerging flux and the background field, we find that larger angles delay filament ascent and eruption timing but facilitate filament formation. Filaments form a hot shell and oscillate with a period of 10 minutes driven by periodic plasma ejections. Repetitive reconnection events inject cold plasma into the self-consistently formed filament channel, introducing a new prominence formation mechanism by flux-emergence-fed injection. Our analysis highlights the dynamic interplay between magnetic reconnection, plasma cooling and heating, and filament dynamics. These findings provide insights into solar eruptions and their observational signatures, emphasizing the role of multi-thermal structures in the corona.

astro-ph.SR↗

arXiv2025

Explore-Construct-Filter: An Automated Framework for Rich and Reliable API Knowledge Graph Construction

The API Knowledge Graph (API KG) is a structured network that models API entities and their relations, providing essential semantic insights for tasks such as API recommendation, code generation, and API misuse detection. However, constructing a knowledge-rich and reliable API KG presents several challenges. Existing schema-based methods rely heavily on manual annotations to design KG schemas, leading to excessive manual overhead. On the other hand, schema-free methods, due to the lack of schema guidance, are prone to introducing noise, reducing the KG's reliability. To address these issues, we propose the Explore-Construct-Filter framework, an automated approach for API KG construction based on large language models (LLMs). This framework consists of three key modules: 1) KG exploration: LLMs simulate the workflow of annotators to automatically design a schema with comprehensive type triples, minimizing human intervention; 2) KG construction: Guided by the schema, LLMs extract instance triples to construct a rich yet unreliable API KG; 3) KG filtering: Removing invalid type triples and suspicious instance triples to construct a rich and reliable API KG. Experimental results demonstrate that our method surpasses the state-of-the-art method, achieving a 25.2% improvement in F1 score. Moreover, the Explore-Construct-Filter framework proves effective, with the KG exploration module increasing KG richness by 133.6% and the KG filtering module improving reliability by 26.6%. Finally, cross-model experiments confirm the generalizability of our framework.

cs.SE↗

arXiv2025

Intention is All You Need: Refining Your Code from Your Intention

Code refinement aims to enhance existing code by addressing issues, refactoring, and optimizing to improve quality and meet specific requirements. As software projects scale in size and complexity, the traditional iterative exchange between reviewers and developers becomes increasingly burdensome. While recent deep learning techniques have been explored to accelerate this process, their performance remains limited, primarily due to challenges in accurately understanding reviewers' intents. This paper proposes an intention-based code refinement technique that enhances the conventional comment-to-code process by explicitly extracting reviewer intentions from the comments. Our approach consists of two key phases: Intention Extraction and Intention Guided Revision Generation. Intention Extraction categorizes comments using predefined templates, while Intention Guided Revision Generation employs large language models (LLMs) to generate revised code based on these defined intentions. Three categories with eight subcategories are designed for comment transformation, which is followed by a hybrid approach that combines rule-based and LLM-based classifiers for accurate classification. Extensive experiments with five LLMs (GPT4o, GPT3.5, DeepSeekV2, DeepSeek7B, CodeQwen7B) under different prompting settings demonstrate that our approach achieves 79% accuracy in intention extraction and up to 66% in code refinement generation. Our results highlight the potential of our approach in enhancing data quality and improving the efficiency of code refinement.

cs.SE↗

arXiv2024

Frozen-field Modeling of Coronal Condensations with MPI-AMRVAC II: Optimization and application in three-dimensional models

The frozen-field hydrodynamic (ffHD) model is a simplification of the full magnetohydrodynamical (MHD) equations under the assumption of a rigid magnetic field, which significantly reduces computational complexity and enhances efficiency. In this work, we combine the ffHD prescription with hyperbolic thermal conduction (TC) and the Transition Region Adaptive Conduction (TRAC) method to achieve further optimization. A series of two-dimensional tests are done to evaluate the performance of the hyperbolic TC and the TRAC method. The results indicate that hyperbolic TC, while showing limiter-affected numerical dissipation, delivers outcomes comparable to classic parabolic TC. The TRAC method effectively compensates for the underestimation of enthalpy flux in low-resolution simulations, as evaluated on tests that demonstrate prominence formation. We present an application of the ffHD model that forms a three-dimensional prominence embedded in a magnetic flux rope, which develops into a stable slab-like filament. The simulation reveals a prominence with an elongated spine and a width consistent with observations, highlighting the potential of the ffHD model in capturing the dynamics of solar prominences. Forward modeling of the simulation data produces synthetic images at various wavelengths, providing insights into the appearance of prominences and filaments in different observational contexts. The ffHD model, with its computational efficiency and the demonstrated capability to simulate complex solar phenomena, offers a valuable tool for solar physicists, and is implemented in the open-source MPI-AMRVAC framework.

astro-ph.SR↗

arXiv2024

Cross-chain Sharing of Personal Health Records: Heterogeneous and Interoperable Blockchains

With the widespread adoption of medical informatics, a wealth of valuable personal health records (PHR) has been generated. Concurrently, blockchain technology has enhanced the security of medical institutions. However, these institutions often function as isolated data silos, limiting the potential value of PHRs. As the demand for data sharing between hospitals on different blockchains grows, addressing the challenge of cross-chain data sharing becomes crucial. When sharing PHRs across blockchains, the limited storage and computational capabilities of medical Internet of Things (IoT) devices complicate the storage of large volumes of PHRs and the handling of complex calculations. Additionally, varying blockchain cryptosystems and the risk of internal attacks further complicate the cross-chain sharing of PHRs. This paper proposes a scheme for sharing PHRs across heterogeneous and interoperable blockchains. Medical IoT devices can encrypt and store real-time PHRs in an InterPlanetary File System, requiring only simple operations for data sharing. An enhanced proxy re-encryption(PRE) algorithm addresses the differences in blockchain cryptosystems. Multi-dimensional analysis demonstrates that this scheme offers robust security and excellent performance.

cs.CR↗

arXiv2024

Multi-label Classification for Android Malware Based on Active Learning

The existing malware classification approaches (i.e., binary and family classification) can barely benefit subsequent analysis with their outputs. Even the family classification approaches suffer from lacking a formal naming standard and an incomplete definition of malicious behaviors. More importantly, the existing approaches are powerless for one malware with multiple malicious behaviors, while this is a very common phenomenon for Android malware in the wild. So, neither of them can provide researchers with a direct and comprehensive enough understanding of malware. In this paper, we propose MLCDroid, an ML-based multi-label classification approach that can directly indicate the existence of pre-defined malicious behaviors. With an in-depth analysis, we summarize six basic malicious behaviors from real-world malware with security reports and construct a labeled dataset. We compare the results of 70 algorithm combinations to evaluate the effectiveness (best at 73.3%). Faced with the challenge of the expensive cost of data annotation, we further propose an active learning approach based on data augmentation, which can improve the overall accuracy to 86.7% with a data augmentation of 5,000+ high-quality samples from an unlabeled malware dataset. This is the first multi-label Android malware classification approach intending to provide more information on fine-grained malicious behaviors.

cs.CR↗

arXiv2024

Compact ultra-broadband light coupling on chip via nonadiabatic pumping

Enlarging bandwidth capacity of the integrated photonic systems demands efficient and broadband light coupling among optical elements, which has been a vital issue in integrated photonics. Here, we have developed a compact ultra-broadband light coupling strategy based on nonadiabatic pumping in coupled optical waveguides, and experimentally demonstrated the designs in thin-film lithium niobate on insulator (LNOI) platform. We found that nonadiabatic transition would produce a decreased dispersion of the phases related to eigenstates in the waveguides. As a consequence, we realized high-efficiency directional transfer between edgestates for various wavelengths covering a 1-dB bandwidth of ~320 nm in experiment (>400 nm in simulation), with a coupling length (~50 μm) approximately 1/10 of that required in the adiabatic regime. Furthermore, we have constructed complex functional devices including beamsplitter and multiple-level cascaded networks for broadband light routing and splitting. Our work preserves significant advantages simultaneously in extending the operation bandwidth and minimizing the footprint, which demonstrates great potential for large-scale and compact photonic integration on chip.

physics.optics↗

arXiv2024

Frozen-field Modeling of Coronal Condensations with MPI-AMRVAC I: Demonstration in two-dimensional models

Large-scale coronal plasma evolutions can be adequately described by magnetohydrodynamics (MHD) equations. However, full multi-dimensional MHD simulations require substantial computational resources. Given the low plasma $β$ in the solar corona, in many coronal studies, it suffices to approximate the magnetic field to remain topologically fixed and effectively conduct one-dimensional (1D) hydrodynamic (HD) simulations instead. This approach is often employed in studies of coronal loops and their liability to form condensations related to thermal instability. While 1D HD simulations along given and fixed field line shapes are convenient and fast, they are difficult to directly compare with multi-dimensional phenomena. Therefore, it is more convenient to solve volume-filling, multi-dimensional versions of the MHD equations where we freeze the magnetic field, transforming it into frozen-field HD (ffHD) equations for simulation. We have incorporated this ffHD module into our open-source MPI-AMRVAC code and tested it using a two-dimensional (2D) evaporation--condensation model to study prominence formation due to radiative losses. The 2D ffHD results are compared with those from actual 2D MHD and pseudo-2D HD simulations, analyzing the differences and their causes. Pseudo-2D studies account for the known {flux tube} expansion effects. Overall, the performance of 2D ffHD is close to that of 2D MHD and pseudo-2D HD. The 2D tests conducted in this paper will be extended in follow-up studies to 3D simulations based on analytical or observational approaches.

astro-ph.SR↗

arXiv2024

FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion

The rise of code pre-trained models has significantly enhanced various coding tasks, such as code completion, and tools like GitHub Copilot. However, the substantial size of these models, especially large models, poses a significant challenge when it comes to fine-tuning them for specific downstream tasks. As an alternative approach, retrieval-based methods have emerged as a promising solution, augmenting model predictions without the need for fine-tuning. Despite their potential, a significant challenge is that the designs of these methods often rely on heuristics, leaving critical questions about what information should be stored or retrieved and how to interpolate such information for augmenting predictions. To tackle this challenge, we first perform a theoretical analysis of the fine-tuning process, highlighting the importance of delta logits as a catalyst for improving model predictions. Building on this insight, we develop a novel retrieval-based method, FT2Ra, which aims to mimic genuine fine-tuning. While FT2Ra adopts a retrieval-based mechanism, it uniquely adopts a paradigm with a learning rate and multi-epoch retrievals, which is similar to fine-tuning.In token-level completion, which represents a relatively easier task, FT2Ra achieves a 4.29% improvement in accuracy compared to the best baseline method on UniXcoder. In the more challenging line-level completion task, we observe a substantial more than twice increase in Exact Match (EM) performance, indicating the significant advantages of our theoretical analysis. Notably, even when operating without actual fine-tuning, FT2Ra exhibits competitive performance compared to the models with real fine-tuning.

cs.SE↗

arXiv2023

EndWatch: A Practical Method for Detecting Non-Termination in Real-World Software

Detecting non-termination is crucial for ensuring program correctness and security, such as preventing denial-of-service attacks. While termination analysis has been studied for many years, existing methods have limited scalability and are only effective on small programs. To address this issue, we propose a practical termination checking technique, called EndWatch, for detecting non-termination caused by infinite loops through testing. Specifically, we introduce two methods to generate non-termination oracles based on checking state revisits, i.e., if the program returns to a previously visited state at the same program location, it does not terminate. The non-termination oracles can be incorporated into testing tools (e.g., AFL used in this paper) to detect non-termination in large programs. For linear loops, we perform symbolic execution on individual loops to infer State Revisit Conditions (SRCs) and instrument SRCs into target loops. For non-linear loops, we instrument target loops for checking concrete state revisits during execution. We evaluated EndWatch on standard benchmarks with small-sized programs and real-world projects with large-sized programs. The evaluation results show that EndWatch is more effective than the state-of-the-art tools on standard benchmarks (detecting 87% of non-terminating programs while the best baseline detects only 67%), and useful in detecting non-termination in real-world projects (detecting 90% of known non-termination CVEs and 4 unknown bugs).

cs.SE↗

arXiv2023

Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study

Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can significantly impact the development process. Recently, ChatGPT, a cutting-edge language model, has demonstrated impressive performance in various natural language processing tasks, suggesting its potential to automate code review processes. However, it is still unclear how well ChatGPT performs in code review tasks. To fill this gap, in this paper, we conduct the first empirical study to understand the capabilities of ChatGPT in code review tasks, specifically focusing on automated code refinement based on given code reviews. To conduct the study, we select the existing benchmark CodeReview and construct a new code review dataset with high quality. We use CodeReviewer, a state-of-the-art code review tool, as a baseline for comparison with ChatGPT. Our results show that ChatGPT outperforms CodeReviewer in code refinement tasks. Specifically, our results show that ChatGPT achieves higher EM and BLEU scores of 22.78 and 76.44 respectively, while the state-of-the-art method achieves only 15.50 and 62.88 on a high-quality code review dataset. We further identify the root causes for ChatGPT's underperformance and propose several strategies to mitigate these challenges. Our study provides insights into the potential of ChatGPT in automating the code review process, and highlights the potential research directions.

cs.SE↗

arXiv2023

Automated and Context-Aware Repair of Color-Related Accessibility Issues for Android Apps

Approximately 15% of the world's population is suffering from various disabilities or impairments. However, many mobile UX designers and developers disregard the significance of accessibility for those with disabilities when developing apps. A large number of studies and some effective tools for detecting accessibility issues have been conducted and proposed to mitigate such a severe problem. However, compared with detection, the repair work is obviously falling behind. Especially for the color-related accessibility issues, which is one of the top issues in apps with a greatly negative impact on vision and user experience. Apps with such issues are difficult to use for people with low vision and the elderly. Unfortunately, such an issue type cannot be directly fixed by existing repair techniques. To this end, we propose Iris, an automated and context-aware repair method to fix the color-related accessibility issues (i.e., the text contrast issues and the image contrast issues) for apps. By leveraging a novel context-aware technique that resolves the optimal colors and a vital phase of attribute-to-repair localization, Iris not only repairs the color contrast issues but also guarantees the consistency of the design style between the original UI page and repaired UI page. Our experiments unveiled that Iris can achieve a 91.38% repair success rate with high effectiveness and efficiency. The usefulness of Iris has also been evaluated by a user study with a high satisfaction rate as well as developers' positive feedback. 9 of 40 submitted pull requests on GitHub repositories have been accepted and merged into the projects by app developers, and another 4 developers are actively discussing with us for further repair. Iris is publicly available to facilitate this new research direction.

cs.SE↗

arXiv2023

Winking filaments due to cyclic evaporation-condensation

Observations have shown that some filaments appear and disappear in the H$α$ line wing images periodically. There have been no attempts to model these "winking filaments" thus far. The evaporation--condensation mechanism is widely used to explain the formation of solar filaments. Here, we demonstrate, for the first time, how multi-dimensional evaporation--condensation in an arcade setup invariably causes a stretching of the magnetic topology. We aim to check whether this magnetic stretching during cyclic evaporation--condensation could reproduce a winking filament. We used our open-source code MPI-AMRVAC to carry out 2D magnetohydrodynamic simulations based on a quadrupolar configuration. A periodic localized heating, which modulates the evaporation--condensation process, was imposed before, during, and after the formation of the filament. Synthetic H$α$ and 304 Å, images were produced to compare the results with observations. For the first time, we noticed the winking filament phenomenon in a simulation of the formation of on-disk solar filaments, which was in good agreement with observations. Typically, the period of the winking is different from the period of the impulsive heating. A forced oscillator model explains this difference and fits the results well. A parameter survey is also done to look into details of the magnetic stretching phenomenon. We found that the stronger the heating or the higher the layer where the heating occurs, the more significant the winking effect appears.

astro-ph.SR↗

arXiv2023

Multi-thermal jet formation triggered by flux emergence

Flux emergence is responsible for various solar eruptions. Combining observation and simulations, we investigate the influence of flux emergence at one footpoint of an arcade on coronal rain as well as induced eruptions. The emergence changes the pressure in the loops, and the internal coronal rain all moves to the other side. The emerging flux reconnects with the overlying magnetic field, forming a current sheet and magnetic islands. The plasma is ejected outwards and heated, forming a cool jet ~ 6000 K and a hot X-ray jet ~ 4 MK simultaneously. The jet dynamical properties agree very well between observation and simulation. In the simulation, the jet also displays transverse oscillations with a period of 8 minutes, a so-called whip-like motion. The movement of the jet and dense plasmoids changes the configuration of the local magnetic field, facilitating the occurrence of Kelvin--Helmholtz instability, and vortex-like structures form at the boundary of the jet. Our simulation clearly demonstrates the effect of emergence on coronal rain, the dynamical details of reconnecting plasmoid chains, the formation of multi-thermal jets, and the cycling of cool mass between the chromosphere and the corona.

astro-ph.SR↗