arXiv subjects

Shaowei Wang

Publications and source records attributed to Shaowei Wang.

At least 73 records · Page 4Linked to original sources

arXiv2024

VFDelta: A Framework for Detecting Silent Vulnerability Fixes by Enhancing Code Change Learning

Vulnerability fixes in open source software (OSS) usually follow the coordinated vulnerability disclosure model and are silently fixed. This delay can expose OSS users to risks as malicious parties might exploit the software before fixes are publicly known. Therefore, it is important to identify vulnerability fixes early and automatically. Existing methods classify vulnerability fixes by learning code change representations from commits, typically by concatenating code changes, which does not effectively highlight nuanced differences. Additionally, previous approaches fine-tune code embedding models and classification models separately, which limits overall effectiveness. We propose VFDelta, a lightweight yet effective framework that embeds code before and after changes using independent models with surrounding code as context. By performing element-wise subtraction on these embeddings, we capture fine-grain changes. Our architecture allows joint training of embedding and classification models, optimizing overall performance. Experiments demonstrate that VFDelta achieves up to 0.33 F1 score and 0.63 CostEffort@5, improving over state-of-the-art methods by 77.4% and 7.1%, respectively. Ablation analysis confirms the importance of our code change representation in capturing small changes. We also expanded the dataset and introduced a temporal split to simulate real-world scenarios; VFDelta significantly outperforms baselines VulFixMiner and MiDas across all metrics in this setting.

cs.SE↗

arXiv2024

Imaginary Poynting momentum driven particle rotation by cylindrically polarized Gaussian beams

Imaginary Poynting momentum (IPM) provides a new degree of freedom for particle manipulation. However, the application of IPM in experiments has been largely unexplored. Here, we demonstrate the IPM driven particle rotation by cylindrically polarized Gaussian beams with no spin or orbital angular momentum. Theoretical analysis and experimental measurements demonstrate that gold microparticles will be rotated in the azimuthal direction while confined in the radial direction. We achieved controllable rotation of the particle by tuning the cylindrical polarization state. Interestingly, the transfer of IPM to a gold particle is demonstrated to be competitive with that of spin angular momentum. These findings hold promising in light-matter interactions and particle manipulations.

physics.optics↗

arXiv2024

GUI Element Detection Using SOTA YOLO Deep Learning Models

Detection of Graphical User Interface (GUI) elements is a crucial task for automatic code generation from images and sketches, GUI testing, and GUI search. Recent studies have leveraged both old-fashioned and modern computer vision (CV) techniques. Oldfashioned methods utilize classic image processing algorithms (e.g. edge detection and contour detection) and modern methods use mature deep learning solutions for general object detection tasks. GUI element detection, however, is a domain-specific case of object detection, in which objects overlap more often, and are located very close to each other, plus the number of object classes is considerably lower, yet there are more objects in the images compared to natural images. Hence, the studies that have been carried out on comparing various object detection models, might not apply to GUI element detection. In this study, we evaluate the performance of the four most recent successful YOLO models for general object detection tasks on GUI element detection and investigate their accuracy performance in detecting various GUI elements.

cs.CV↗

arXiv2024

Privacy Amplification via Shuffling: Unified, Simplified, and Tightened

The shuffle model of differential privacy provides promising privacy-utility balances in decentralized, privacy-preserving data analysis. However, the current analyses of privacy amplification via shuffling lack both tightness and generality. To address this issue, we propose the \emph{variation-ratio reduction} as a comprehensive framework for privacy amplification in both single-message and multi-message shuffle protocols. It leverages two new parameterizations: the total variation bounds of local messages and the probability ratio bounds of blanket messages, to determine indistinguishability levels. Our theoretical results demonstrate that our framework provides tighter bounds, especially for local randomizers with extremal probability design, where our bounds are exactly tight. Additionally, variation-ratio reduction complements parallel composition in the shuffle model, yielding enhanced privacy accounting for popular sampling-based randomizers employed in statistical queries (e.g., range queries, marginal queries, and frequent itemset mining). Empirical findings demonstrate that our numerical amplification bounds surpass existing ones, conserving up to $30\%$ of the budget for single-message protocols, $75\%$ for multi-message ones, and a striking $75\%$-$95\%$ for parallel composition. Our bounds also result in a remarkably efficient $\tilde{O}(n)$ algorithm that numerically amplifies privacy in less than $10$ seconds for $n=10^8$ users.

cs.CR↗

arXiv2024

SimClone: Detecting Tabular Data Clones using Value Similarity

Data clones are defined as multiple copies of the same data among datasets. Presence of data clones between datasets can cause issues such as difficulties in managing data assets and data license violations when using datasets with clones to build AI software. However, detecting data clones is not trivial. Majority of the prior studies in this area rely on structural information to detect data clones (e.g., font size, column header). However, tabular datasets used to build AI software are typically stored without any structural information. In this paper, we propose a novel method called SimClone for data clone detection in tabular datasets without relying on structural information. SimClone method utilizes value similarities for data clone detection. We also propose a visualization approach as a part of our SimClone method to help locate the exact position of the cloned data between a dataset pair. Our results show that our SimClone outperforms the current state-of-the-art method by at least 20\% in terms of both F1-score and AUC. In addition, SimClone's visualization component helps identify the exact location of the data clone in a dataset with a Precision@10 value of 0.80 in the top 20 true positive predictions.

cs.DB↗

arXiv2024

An Empirical Study on the Effectiveness of Large Language Models for SATD Identification and Classification

Self-Admitted Technical Debt (SATD), a concept highlighting sub-optimal choices in software development documented in code comments or other project resources, poses challenges in the maintainability and evolution of software systems. Large language models (LLMs) have demonstrated significant effectiveness across a broad range of software tasks, especially in software text generation tasks. Nonetheless, their effectiveness in tasks related to SATD is still under-researched. In this paper, we investigate the efficacy of LLMs in both identification and classification of SATD. For both tasks, we investigate the performance gain from using more recent LLMs, specifically the Flan-T5 family, across different common usage settings. Our results demonstrate that for SATD identification, all fine-tuned LLMs outperform the best existing non-LLM baseline, i.e., the CNN model, with a 4.4% to 7.2% improvement in F1 score. In the SATD classification task, while our largest fine-tuned model, Flan-T5-XL, still led in performance, the CNN model exhibited competitive results, even surpassing four of six LLMs. We also found that the largest Flan-T5 model, i.e., Flan-T5-XXL, when used with a zero-shot in-context learning (ICL) approach for SATD identification, provides competitive results with traditional approaches but performs 6.4% to 9.2% worse than fine-tuned LLMs. For SATD classification, few-shot ICL approach, incorporating examples and category descriptions in prompts, outperforms the zero-shot approach and even surpasses the fine-tuned smaller Flan-T5 models. Moreover, our experiments demonstrate that incorporating contextual information, such as surrounding code, into the SATD classification task enables larger fine-tuned LLMs to improve their performance.

cs.SE↗

arXiv2024

Towards Better Graph Neural Network-based Fault Localization Through Enhanced Code Representation

Automatic software fault localization plays an important role in software quality assurance by pinpointing faulty locations for easier debugging. Coverage-based fault localization, a widely used technique, employs statistics on coverage spectra to rank code based on suspiciousness scores. However, the rigidity of statistical approaches calls for learning-based techniques. Amongst all, Grace, a graph-neural network (GNN) based technique has achieved state-of-the-art due to its capacity to preserve coverage spectra, i.e., test-to-source coverage relationships, as precise abstract syntax-enhanced graph representation, mitigating the limitation of other learning-based technique which compresses the feature representation. However, such representation struggles with scalability due to the increasing complexity of software and associated coverage spectra and AST graphs. In this work, we proposed a new graph representation, DepGraph, that reduces the complexity of the graph representation by 70% in nodes and edges by integrating interprocedural call graph in the graph representation of the code. Moreover, we integrate additional features such as code change information in the graph as attributes so the model can leverage rich historical project data. We evaluate DepGraph using Defects4j 2.0.0, and it outperforms Grace by locating 20% more faults in Top-1 and improving the Mean First Rank (MFR) and the Mean Average Rank (MAR) by over 50% while decreasing GPU memory usage by 44% and training/inference time by 85%. Additionally, in cross-project settings, DepGraph surpasses the state-of-the-art baseline with a 42% higher Top-1 accuracy, and 68% and 65% improvement in MFR and MAR, respectively. Our study demonstrates DepGraph's robustness, achieving state-of-the-art accuracy and scalability for future extension and adoption.

cs.SE↗

arXiv2024

LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing

Logs are important in modern software development with runtime information. Log parsing is the first step in many log-based analyses, that involve extracting structured information from unstructured log data. Traditional log parsers face challenges in accurately parsing logs due to the diversity of log formats, which directly impacts the performance of downstream log-analysis tasks. In this paper, we explore the potential of using Large Language Models (LLMs) for log parsing and propose LLMParser, an LLM-based log parser based on generative LLMs and few-shot tuning. We leverage four LLMs, Flan-T5-small, Flan-T5-base, LLaMA-7B, and ChatGLM-6B in LLMParsers. Our evaluation of 16 open-source systems shows that LLMParser achieves statistically significantly higher parsing accuracy than state-of-the-art parsers (a 96% average parsing accuracy). We further conduct a comprehensive empirical analysis on the effect of training size, model size, and pre-training LLM on log parsing accuracy. We find that smaller LLMs may be more effective than more complex LLMs; for instance where Flan-T5-base achieves comparable results as LLaMA-7B with a shorter inference time. We also find that using LLMs pre-trained using logs from other systems does not always improve parsing accuracy. While using pre-trained Flan-T5-base shows an improvement in accuracy, pre-trained LLaMA results in a decrease (decrease by almost 55% in group accuracy). In short, our study provides empirical evidence for using LLMs for log parsing and highlights the limitations and future research direction of LLM-based log parsers.

cs.SE↗

arXiv2024

Studying and Recommending Information Highlighting in Stack Overflow Answers

Context: Navigating the knowledge of Stack Overflow (SO) remains challenging. To make the posts vivid to users, SO allows users to write and edit posts with Markdown or HTML so that users can leverage various formatting styles (e.g., bold, italic, and code) to highlight the important information. Nonetheless, there have been limited studies on the highlighted information. Objective: We carried out the first large-scale exploratory study on the information highlighted in SO answers in our recent study. To extend our previous study, we develop approaches to automatically recommend highlighted content with formatting styles using neural network architectures initially designed for the Named Entity Recognition task. Method: In this paper, we studied 31,169,429 answers of Stack Overflow. For training recommendation models, we choose CNN-based and BERT-based models for each type of formatting (i.e., Bold, Italic, Code, and Heading) using the information highlighting dataset we collected from SO answers. Results: Our models achieve a precision ranging from 0.50 to 0.72 for different formatting types. It is easier to build a model to recommend Code than other types. Models for text formatting types (i.e., Heading, Bold, and Italic) suffer low recall. Our analysis of failure cases indicates that the majority of the failure cases are due to missing identification. One explanation is that the models are easy to learn the frequent highlighted words while struggling to learn less frequent words (i.g., long-tail knowledge). Conclusion: Our findings suggest that it is possible to develop recommendation models for highlighting information for answers with different formatting styles on Stack Overflow.

cs.CL↗

arXiv2024

AIGC-Assisted Digital Watermark Services in Low-Earth Orbit Satellite-Terrestrial Edge Networks

Low Earth Orbit (LEO) satellite communication is a crucial component of future 6G communication networks, contributing to the development of an integrated satellite-terrestrial network. In the forthcoming satellite-to-ground network, the idle computational resources of LEO satellites can serve as edge servers, delivering intelligent task computation services to ground users. Existing research on satellite-to-ground computation primarily focuses on designing efficient task scheduling algorithms to provide straightforward computation services to ground users. This study aims to integrate satellite edge networks with Artificial Intelligence-Generated Content (AIGC) technology to offer personalized AIGC services to ground users, such as customized digital watermarking services. Firstly, we propose a satellite-to-ground edge network architecture, enabling bidirectional communication between visible LEO satellites and ground users. Each LEO satellite is equipped with intelligent algorithms supporting various AIGC-assisted digital watermarking technologies with different precision levels. Secondly, considering metrics like satellite visibility, satellite-to-ground communication stability, digital watermark quality, satellite-to-ground communication time, digital watermarking time, and ground user energy consumption, we construct an AIGC-assisted digital watermarking model based on the satellite-to-ground edge network. Finally, we introduce a reinforcement learning-based task scheduling algorithm to obtain an optimal strategy. Experimental results demonstrate that our approach effectively meets the watermark generation needs of ground users, achieving a well-balanced trade-off between generation time and user energy consumption. We anticipate that this work will provide an effective solution for the intelligent services in satellite-to-ground edge networks.

cs.NI↗

arXiv2024

Instant square lattice structured illumination microscopy: an optimal strategy towards photon-saving and real-time super-resolution observation

Over the past decade, structured illumination microscopy (SIM) has found its niche in super-resolution (SR) microscopy due to its fast imaging speed and low excitation intensity. However, due to the significantly higher light dose compared to wide-field microscopy and the time-consuming post-processing procedures, long-term, real-time, super-resolution observation of living cells is still out of reach for most SIM setups, which inevitably limits its routine use by cell biologists. Here, we describe square lattice SIM (SL-SIM) for long-duration live cell imaging by using the square lattice optical field as illumination, which allows continuous super-resolved observation over long periods of time. In addition, by extending the previous joint spatial-frequency reconstruction concept to SL-SIM, a high-speed reconstruction strategy is validated in the GPU environment, whose reconstruction time is even shorter than image acquisition time, thus enabling real-time observation. We have demonstrated the potential of SL-SIM on various biological applications, ranging from microtubule cytoskeleton dynamics to the interactions of mitochondrial cristae and DNAs in COS7 cells. The inherent lower light dose and user-friendly workflow of the SL-SIM could help make long-duration, real-time and super-resolved observations accessible to biological laboratories.

physics.optics↗

arXiv2024

Machine Unlearning in Large Language Models

Recently, large language models (LLMs) have emerged as a notable field, attracting significant attention for its ability to automatically generate intelligent contents for various application domains. However, LLMs still suffer from significant security and privacy issues. For example, LLMs might expose user privacy from hacking attacks or targeted prompts. To address this problem, this paper introduces a novel machine unlearning framework into LLMs. Our objectives are to make LLMs not produce harmful, hallucinatory, or privacy-compromising responses, while retaining their standard output capabilities. To accomplish this, we use an evaluative model to pinpoint dialogues needing unlearning. We also establish a distance loss to function as the model's negative loss, diverting it from previous undesirable outputs. Furthermore, we determine the expected output's cluster mean to formulate a positive loss, directing the model's outputs toward preferable outcomes without compromising its reasoning abilities and performance. Experimental results show that our approach effectively meets unlearning objectives without substantially compromising model performance.

cs.CR↗

arXiv2024

Heating from Above in Non-scattering Suspensions: Phototactic Bioconvection under Collimated Irradiation

Examining phototactic bioconvection in non-scattering suspensions with upper heating and collimated irradiation, this study delves into the intricate dynamics influenced by light and microorganisms. The study focuses on the linear stability of the basic state, examining neutral curves. The numerical analysis involves solving a system of equations using the MATLAB bvp4c solver. The investigation considers the impact of parameters, such as the thermal Rayleigh number, critical total intensity, and Lewis number, on the critical bioconvection Rayleigh number. As the critical total intensity varies, a transition from a stationary to an oscillatory solution (and vice versa) is observed. Phototactic microorganisms are incorporated into the model, and results show how varying parameters affect convection patterns and stability. The findings reveal interesting phenomena, including Hopf bifurcations and limit cycles.

math.DS↗

arXiv2024

Federated Unlearning for Human Activity Recognition

The rapid evolution of Internet of Things (IoT) technology has spurred the widespread adoption of Human Activity Recognition (HAR) in various daily life domains. Federated Learning (FL) is frequently utilized to build a global HAR model by aggregating user contributions without transmitting raw individual data. Despite substantial progress in user privacy protection with FL, challenges persist. Regulations like the General Data Protection Regulation (GDPR) empower users to request data removal, raising a new query in FL: How can a HAR client request data removal without compromising other clients' privacy? In response, we propose a lightweight machine unlearning method for refining the FL HAR model by selectively removing a portion of a client's training data. Our method employs a third-party dataset unrelated to model training. Using KL divergence as a loss function for fine-tuning, we aim to align the predicted probability distribution on forgotten data with the third-party dataset. Additionally, we introduce a membership inference evaluation method to assess unlearning effectiveness. Experimental results across diverse datasets show our method achieves unlearning accuracy comparable to \textit{retraining} methods, resulting in speedups ranging from hundreds to thousands.

cs.LG↗

arXiv2023

High-speed image reconstruction for nonlinear structured illumination microscopy

By exploiting the nonlinear responses of the fluorescent probes, the spatial resolution of structured illumination microscopy(SIM) can be further increased. However, due to the complex reconstruction process, the traditional reconstruction method of nonlinear structured illumination microscopy (NL-SIM) is relatively slow, which brings a great challenge to realizing real-time display of super-resolution results. To address these issues, an accelerated NL-SIM reconstruction algorithm was developed by extending a high-speed reconstruction framework, Joint Space and Frequency Reconstruction (JSFR) to NL-SIM. We anticipate that this algorithm will facilitate NL- SIM becoming a routine tool in biomedical laboratories.

physics.optics↗

arXiv2023

Thermal-bioconvection in a non-scattering suspension of phototactic microorganisms

This article investigates the linear stability of thermal-bioconvection within a suspension containing phototactic microorganisms heated from below. In suspension, the upper surface is taken as stress-free, while the lower surface is taken as rigid. The resulting eigenvalue problem, including the bioconvection Rayleigh and thermal Rayleigh numbers, is resolved numerically. Changes in the critical total intensity and Lewis number do not impact the critical threshold of the thermal Rayleigh number; however, they notably influence the critical bioconvection Rayleigh number. The critical total intensity and Lewis number destabilize the suspension. It is observed that heating from below enhances the instability of the layer. At higher temperatures, Rayleigh-B$\acute{e}$nard convection dominates bioconvection, resulting in a single convection cell.

physics.flu-dyn↗

arXiv2023

Effect of thermal-bioconvection in a rotating phototactic medium

In this article, thermal bioconvection in a rotating phototactic medium is analyzed with a stress-free top and rigid upper boundary. The suspension rotates with a uniform angular velocity around a vertical axis. Utilizing MATLAB's bvp$4$c solver, neutral and growth rate curves are analyzed, emphasizing the impacts of parameters such as the Taylor number, thermal Rayleigh number, Lewis number, and critical total intensity. It is observed that generally critical bioconvection Rayleigh number increases with increasing Taylor number and Lewis number while decreasing with higher thermal Rayleigh number and critical total intensity. The critical thermal Rayleigh number appears to remain unaffected by variations in the critical total intensity and Lewis number. However, it is notably influenced by changes in the Taylor number.

physics.flu-dyn↗

arXiv2023

BAGEL: Backdoor Attacks against Federated Contrastive Learning

Federated Contrastive Learning (FCL) is an emerging privacy-preserving paradigm in distributed learning for unlabeled data. In FCL, distributed parties collaboratively learn a global encoder with unlabeled data, and the global encoder could be widely used as a feature extractor to build models for many downstream tasks. However, FCL is also vulnerable to many security threats (e.g., backdoor attacks) due to its distributed nature, which are seldom investigated in existing solutions. In this paper, we study the backdoor attack against FCL as a pioneer research, to illustrate how backdoor attacks on distributed local clients act on downstream tasks. Specifically, in our system, malicious clients can successfully inject a backdoor into the global encoder by uploading poisoned local updates, thus downstream models built with this global encoder will also inherit the backdoor. We also investigate how to inject backdoors into multiple downstream models, in terms of two different backdoor attacks, namely the \textit{centralized attack} and the \textit{decentralized attack}. Experiment results show that both the centralized and the decentralized attacks can inject backdoors into downstream models effectively with high attack success rates. Finally, we evaluate two defense methods against our proposed backdoor attacks in FCL, which indicates that the decentralized backdoor attack is more stealthy and harder to defend.

cs.CR↗