arXiv subjects

Lu Xiao

Publications and source records attributed to Lu Xiao.

14 recordsLinked to original sources

arXiv2026

Identifying Concurrency Bug Reports via Linguistic Patterns

With the growing ubiquity of multi-core architectures, concurrent systems have become essential but increasingly prone to complex issues such as data races and deadlocks. While modern issue-tracking systems facilitate the reporting of such problems, labeling concurrency-related bug reports remains a labor-intensive and error-prone task. This paper presents a linguistic-pattern-based framework for automatically identifying concurrency bug reports. We derive 58 distinct linguistic patterns from 730 manually labeled concurrency bug reports, organized across four levels: word-level (keywords), phrase-level (n-grams), sentence-level (semantic), and bug report-level (contextual). To assess their effectiveness, we evaluate four complementary approaches-matching, learning, prompt-based, and fine-tuning-spanning traditional machine learning, large language models (LLMs), and pre-trained language models (PLMs). Our comprehensive evaluation on 12 large-scale open-source projects (10,920 issue reports from GitHub and Jira) demonstrates that fine-tuning PLMs with linguistic-pattern-enriched inputs achieves the best performance, reaching a precision of 91% on GitHub and 93% on Jira, and maintaining strong precision on post cut-off data (91%). The contributions of this work include: (1) a comprehensive taxonomy of linguistic patterns for concurrency bugs, (2) a novel fine-tuning strategy that integrates domain-specific linguistic knowledge into PLMs, and (3) a curated, labeled dataset to support reproducible research. Together, these advances provide a foundation for improving the automation, precision, and interpretability of concurrency bug classification.

cs.SE↗

arXiv2025

Sparse-Up: Learnable Sparse Upsampling for 3D Generation with High-Fidelity Textures

The creation of high-fidelity 3D assets is often hindered by a 'pixel-level pain point': the loss of high-frequency details. Existing methods often trade off one aspect for another: either sacrificing cross-view consistency, resulting in torn or drifting textures, or remaining trapped by the resolution ceiling of explicit voxels, forfeiting fine texture detail. In this work, we propose Sparse-Up, a memory-efficient, high-fidelity texture modeling framework that effectively preserves high-frequency details. We use sparse voxels to guide texture reconstruction and ensure multi-view consistency, while leveraging surface anchoring and view-domain partitioning to break through resolution constraints. Surface anchoring employs a learnable upsampling strategy to constrain voxels to the mesh surface, eliminating over 70% of redundant voxels present in traditional voxel upsampling. View-domain partitioning introduces an image patch-guided voxel partitioning scheme, supervising and back-propagating gradients only on visible local patches. Through these two strategies, we can significantly reduce memory consumption during high-resolution voxel training without sacrificing geometric consistency, while preserving high-frequency details in textures.

cs.CV↗

arXiv2025

Swift4D:Adaptive divide-and-conquer Gaussian Splatting for compact and efficient reconstruction of dynamic scene

Novel view synthesis has long been a practical but challenging task, although the introduction of numerous methods to solve this problem, even combining advanced representations like 3D Gaussian Splatting, they still struggle to recover high-quality results and often consume too much storage memory and training time. In this paper we propose Swift4D, a divide-and-conquer 3D Gaussian Splatting method that can handle static and dynamic primitives separately, achieving a good trade-off between rendering quality and efficiency, motivated by the fact that most of the scene is the static primitive and does not require additional dynamic properties. Concretely, we focus on modeling dynamic transformations only for the dynamic primitives which benefits both efficiency and quality. We first employ a learnable decomposition strategy to separate the primitives, which relies on an additional parameter to classify primitives as static or dynamic. For the dynamic primitives, we employ a compact multi-resolution 4D Hash mapper to transform these primitives from canonical space into deformation space at each timestamp, and then mix the static and dynamic primitives to produce the final output. This divide-and-conquer method facilitates efficient training and reduces storage redundancy. Our method not only achieves state-of-the-art rendering quality while being 20X faster in training than previous SOTA methods with a minimum storage requirement of only 30MB on real-world datasets. Code is available at https://github.com/WuJH2001/swift4d.

cs.CV↗

arXiv2024

A Systematic Mapping Study on Architectural Approaches to Software Performance Analysis

Software architecture is the foundation of a system's ability to achieve various quality attributes, including software performance. However, there lacks comprehensive and in-depth understanding of why and how software architecture and performance analysis are integrated to guide related future research. To fill this gap, this paper presents a systematic mapping study of 109 papers that integrate software architecture and performance analysis. We focused on five research questions that provide guidance for researchers and practitioners to gain an in-depth understanding of this research area. These questions addressed: a systematic mapping of related studies based on the high-level research purposes and specific focuses (RQ1), the software development activities these studies intended to facilitate (RQ2), the typical study templates of different research purposes (RQ3), the available tools and instruments for automating the analysis (RQ4), and the evaluation methodology employed in the studies (RQ5). Through these research questions, we also identified critical research gaps and future directions, including: 1) the lack of available tools and benchmark datasets to support replication, cross-validation and comparison of studies; 2) the need for architecture and performance analysis techniques that handle the challenges in emerging software domains; 3) the lack of consideration of practical factors that impact the adoption of the architecture and performance analysis approaches; and finally 4) the need for the adoption of modern ML/AI techniques to efficiently integrate architecture and performance analysis.

cs.SE↗

arXiv2024

HDRGS: High Dynamic Range Gaussian Splatting

Recent years have witnessed substantial advancements in the field of 3D reconstruction from 2D images, particularly following the introduction of the neural radiance field (NeRF) technique. However, reconstructing a 3D high dynamic range (HDR) radiance field, which aligns more closely with real-world conditions, from 2D multi-exposure low dynamic range (LDR) images continues to pose significant challenges. Approaches to this issue fall into two categories: grid-based and implicit-based. Implicit methods, using multi-layer perceptrons (MLP), face inefficiencies, limited solvability, and overfitting risks. Conversely, grid-based methods require significant memory and struggle with image quality and long training times. In this paper, we introduce Gaussian Splatting-a recent, high-quality, real-time 3D reconstruction technique-into this domain. We further develop the High Dynamic Range Gaussian Splatting (HDR-GS) method, designed to address the aforementioned challenges. This method enhances color dimensionality by including luminance and uses an asymmetric grid for tone-mapping, swiftly and precisely converting pixel irradiance to color. Our approach improves HDR scene recovery accuracy and integrates a novel coarse-to-fine strategy to speed up model convergence, enhancing robustness against sparse viewpoints and exposure extremes, and preventing local optima. Extensive testing confirms that our method surpasses current state-of-the-art techniques in both synthetic and real-world scenarios.

cs.CV↗

arXiv2024

Topological magnons in a collinear altermagnet

We propose a model with Weyl magnons and nodal-line magnons ($\mathbb{Z}_2$) in a collinear altermagnet on the honeycomb lattice. Altermagnetic magnon bands can be realized by breaking the symmetry of the second nearest neighbor exchange couplings without the Dzyaloshinskii-Moriya interaction (DMI). In addition to the Chern number and $\mathbb{Z}_2$ invariant, chirality is important to describe the magnon topology. The model shows the nonzero magnon spin Nernst effect when a longitudinal temperature gradient exists. We calculate the differential gyromagnetic ratio induced purely by the topology of altermagnetic magnons, which can be probed through the Einstein-de Haas (EdH) effect.

cond-mat.mes-hall↗

arXiv2024

How Do Developers Structure Unit Test Cases? An Empirical Study from the "AAA" Perspective

The AAA pattern, i.e. arrange, act, and assert, provides a unified structure for unit test cases, which benefits comprehension and maintenance. However, there is little understanding regarding whether and how common real-life developers structure unit test cases following AAA in practice. In particular, are there recurring anti-patterns that deviate from the AAA structure and merit refactoring? And, if test cases follow the AAA structure, could they contain design flaws in the A blocks? If we propose refactoring to fix the design of test cases following the AAA, how do developers receive the proposals? Do they favor refactoring? If not, what are their considerations? This study presents an empirical study on 435 real-life unit test cases randomly selected from four open-source projects. Overall, the majority (71.5%) of test cases follow the AAA structure. And, we observed three recurring anti-patterns that deviate from the AAA structure, as well as four design flaws that may reside inside of the A blocks. Each issue type has its drawbacks and merits corresponding refactoring resolutions. We sent a total of 18 refactoring proposals as issue tickets for fixing these problems. We received 78% positive feedback favoring the refactoring. From the rejections, we learned that return-on-investment is a key consideration for developers. The findings provide insights for practitioners to structure unit test cases with AAA in mind, and for researchers to develop related techniques for enforcing AAA in test cases.

cs.SE↗

arXiv2024

Explaining the Contributing Factors for Vulnerability Detection in Machine Learning

There is an increasing trend to mine vulnerabilities from software repositories and use machine learning techniques to automatically detect software vulnerabilities. A fundamental but unresolved research question is: how do different factors in the mining and learning process impact the accuracy of identifying vulnerabilities in software projects of varying characteristics? Substantial research has been dedicated in this area, including source code static analysis, software repository mining, and NLP-based machine learning. However, practitioners lack experience regarding the key factors for building a baseline model of the state-of-the-art. In addition, there lacks of experience regarding the transferability of the vulnerability signatures from project to project. This study investigates how the combination of different vulnerability features and three representative machine learning models impact the accuracy of vulnerability detection in 17 real-world projects. We examine two types of vulnerability representations: 1) code features extracted through NLP with varying tokenization strategies and three different embedding techniques (bag-of-words, word2vec, and fastText) and 2) a set of eight architectural metrics that capture the abstract design of the software systems. The three machine learning algorithms include a random forest model, a support vector machines model, and a residual neural network model. The analysis shows a recommended baseline model with signatures extracted through bag-of-words embedding, combined with the random forest, consistently increases the detection accuracy by about 4% compared to other combinations in all 17 projects. Furthermore, we observe the limitation of transferring vulnerability signatures across domains based on our experiments.

cs.SE↗

arXiv2022

A nanodiamonds-engineered optical-fiber plasmonic interface for sensitivity-enhanced biosensing

Benefitting from the excellent characteristics such as low cytotoxicity, functionalization versatility, and tunable fluorescence, nanodiamonds (NDs) have shown enormous application potentials in the biomedical field. Herein, we proposed, for the first time to our best knowledge, to integrate NDs on a plasmonic interface constructed on a side-polished fiber using drop-casting method. The added NDs engineers the plasmonic interface towards improving the sensing field, thus enhancing the sensitivity, which, moreover, is significantly dependent on the number of drop-casting cycles (DCs) and the used concentration of NDs dispersion solution. Experimental results suggest that properly increasing the NDs dispersion concentration is beneficial to obtain a higher sensitivity while using a fewer number of DCs, but the excessive concentration extremely deteriorates the resonance dip. Experimentally, using the optimal 0.2 mg/mL concentration and 3 DCs, we achieve the highest RI sensitivity of 3582 nm/RIU, which shows an enhancement of 73.8% compared to the case without NDs modification. The sensitivity enhancement in biosensing is also proved by employing bovine serum albumin as a demo. The behind mechanism is explored via characterizations and simulations. This work opens up a new application form for NDs, i.e. integrating NDs with a plasmonic interface towards high-performance biosensing.

physics.optics↗

arXiv2020

syrapropa at SemEval-2020 Task 11: BERT-based Models Design For Propagandistic Technique and Span Detection

This paper describes the BERT-based models proposed for two subtasks in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. We first build the model for Span Identification (SI) based on SpanBERT, and facilitate the detection by a deeper model and a sentence-level representation. We then develop a hybrid model for the Technique Classification (TC). The hybrid model is composed of three submodels including two BERT models with different training methods, and a feature-based Logistic Regression model. We endeavor to deal with imbalanced dataset by adjusting cost function. We are in the seventh place in SI subtask (0.4711 of F1-measure), and in the third place in TC subtask (0.6783 of F1-measure) on the development set.

cs.CL↗

arXiv2015

Design for Online Deliberative Processes and Technologies: Towards a Multidisciplinary Research Agenda

There has been rapidly growing interest in studying and designing online deliberative processes and technologies. This SIG aims at providing a venue for continuous and constructive dialogue between social, political and cognitive sciences as well as computer science, HCI, and CSCW. Through an online community and a modified version of world cafe discussions, we contribute to the definition of the theoretical building blocks, the identification of a research agenda for the CHI community, and the network of individuals from academia, industry, and the public sector who share interests in different aspects of online deliberation.

cs.HC↗

arXiv2014

Collective intelligence in Massive Online Dialogues

The emergence and ongoing development of Web 2.0 technologies have enabled new and advanced forms of collective intelligence at unprecedented scales, allowing large numbers of individuals to act collectively and create high quality intellectual artifacts. However, little is known about how and when they indeed promote collective intelligence. In this manuscript, we provide a survey of the automated tools developed to analyze discourse-centric collective intelligence. By conducting a thematic analysis of the current research direction, a set of gaps and limitations are identified.

cs.SI↗

arXiv2012

Rationale awareness for quality assurance in iterative human computation processes

Human computation refers to the outsourcing of computation tasks to human workers. It offers a new direction for solving a variety of problems and calls for innovative ways of managing human computation processes. The majority of human computation tasks take a parallel approach, whereas the potential of an iterative approach, i.e., having workers iteratively build on each other's work, has not been sufficiently explored. This study investigates whether and how human workers' awareness of previous workers' rationales affects the performance of the iterative approach in a brainstorming task and a rating task. Rather than viewing this work as a conclusive piece, the author believes that this research endeavor is just the beginning of a new research focus that examines and supports meta-cognitive processes in crowdsourcing activities.

cs.HC↗

arXiv2011

A MovingObject Index for Efficient Query Processing with Peer-Wise Location Privacy

With the growing use of location-based services, location privacy attracts increasing attention from users, industry, and the research community. While considerable effort has been devoted to inventing techniques that prevent service providers from knowing a user's exact location, relatively little attention has been paid to enabling so-called peer-wise privacy--the protection of a user's location from unauthorized peer users. This paper identifies an important efficiency problem in existing peer-privacy approaches that simply apply a filtering step to identify users that are located in a query range, but that do not want to disclose their location to the querying peer. To solve this problem, we propose a novel, privacy-policy enabled index called the PEB-tree that seamlessly integrates location proximity and policy compatibility. We propose efficient algorithms that use the PEB-tree for processing privacy-aware range and kNN queries. Extensive experiments suggest that the PEB-tree enables efficient query processing.

cs.DB↗