arXiv subjects

Song Wang

Publications and source records attributed to Song Wang.

At least 289 records · Page 16Linked to original sources

arXiv2024

Advancing EEG/MEG Source Imaging with Geometric-Informed Basis Functions

Electroencephalography (EEG) and Magnetoencephalography (MEG) are pivotal in understanding brain activity but are limited by their poor spatial resolution. EEG/MEG source imaging (ESI) infers the high-resolution electric field distribution in the brain based on the low-resolution scalp EEG/MEG observations. However, the ESI problem is ill-posed, and how to bring neuroscience priors into ESI method is the key. Here, we present a novel method which utilizes the Brain Geometric-informed Basis Functions (GBFs) as priors to enhance EEG/MEG source imaging. Through comprehensive experiments on both synthetic data and real task EEG data, we demonstrate the superiority of GBFs over traditional spatial basis functions (e.g., Harmonic and MSP), as well as existing ESI methods (e.g., dSPM, MNE, sLORETA, eLORETA). GBFs provide robust ESI results under different noise levels, and result in biologically interpretable EEG sources. We believe the high-resolution EEG source imaging from GBFs will greatly advance neuroscience research.

eess.SP↗

arXiv2024

Evaluating the Effectiveness of GPT-4 Turbo in Creating Defeaters for Assurance Cases

Assurance cases (ACs) are structured arguments that support the verification of the correct implementation of systems' non-functional requirements, such as safety and security, thereby preventing system failures which could lead to catastrophic outcomes, including loss of lives. ACs facilitate the certification of systems in accordance with industrial standards, for example, DO-178C and ISO 26262. Identifying defeaters arguments that refute these ACs is essential for improving the robustness and confidence in ACs. To automate this task, we introduce a novel method that leverages the capabilities of GPT-4 Turbo, an advanced Large Language Model (LLM) developed by OpenAI, to identify defeaters within ACs formalized using the Eliminative Argumentation (EA) notation. Our initial evaluation gauges the model's proficiency in understanding and generating arguments within this framework. The findings indicate that GPT-4 Turbo excels in EA notation and is capable of generating various types of defeaters.

cs.SE↗

arXiv2024

I came, I saw, I certified: some perspectives on the safety assurance of cyber-physical systems

The execution failure of cyber-physical systems (e.g., autonomous driving systems, unmanned aerial systems, and robotic systems) could result in the loss of life, severe injuries, large-scale environmental damage, property destruction, and major economic loss. Hence, such systems usually require a strong justification that they will effectively support critical requirements (e.g., safety, security, and reliability) for which they were designed. Thus, it is often mandatory to develop compelling assurance cases to support that justification and allow regulatory bodies to certify such systems. In such contexts, detecting assurance deficits, relying on patterns to improve the structure of assurance cases, improving existing assurance case notations, and (semi-)automating the generation of assurance cases are key to develop compelling assurance cases and foster consumer acceptance. We therefore explore challenges related to such assurance enablers and outline some potential directions that could be explored to tackle them.

cs.SE↗

arXiv2024

A Survey on Query-based API Recommendation

Application Programming Interfaces (APIs) are designed to help developers build software more effectively. Recommending the right APIs for specific tasks has gained increasing attention among researchers and developers in recent years. To comprehensively understand this research domain, we have surveyed to analyze API recommendation studies published in the last 10 years. Our study begins with an overview of the structure of API recommendation tools. Subsequently, we systematically analyze prior research and pose four key research questions. For RQ1, we examine the volume of published papers and the venues in which these papers appear within the API recommendation field. In RQ2, we categorize and summarize the prevalent data sources and collection methods employed in API recommendation research. In RQ3, we explore the types of data and common data representations utilized by API recommendation approaches. We also investigate the typical data extraction procedures and collection approaches employed by the existing approaches. RQ4 delves into the modeling techniques employed by API recommendation approaches, encompassing both statistical and deep learning models. Additionally, we compile an overview of the prevalent ranking strategies and evaluation metrics used for assessing API recommendation tools. Drawing from our survey findings, we identify current challenges in API recommendation research that warrant further exploration, along with potential avenues for future research.

cs.IR↗

arXiv2024

Stellar cycle and evolution of polar spots in an M+WD binary

Stellar activity cycles reveal continuous relaxation and induction of magnetic fields. The activity cycle is typically traced through the observation of cyclic variations in total brightness or Ca H&K emission flux of stars, as well as cyclic variations of orbital periods of binary systems. In this work, we report the identification of a semi-detached binary system (TIC 16320250) consisting of a white dwarf (0.67 $M_{\odot}$) and an active M dwarf (0.56 $M_{\odot}$). The long-term multi-band optical light curves spanning twenty years revealed three repeated patterns, suggestive of a possible activity cycle of about ten years of the M dwarf. Light curve fitting indicates the repeated variation is caused by the evolution, particularly the motion, of polar spots. The significant Ca H&K, H$α$, ultra-violet, and X-ray emissions imply that the M dwarf is one of the most magnetically active stars. We propose that in the era of large time-domain photometric sky surveys (e.g., ASAS-SN, ZTF, LSST, Sitian), long-term light curve modeling can be a valuable tool for tracing and revealing stellar activity cycle, especially for stars in binary systems.

astro-ph.SR↗

arXiv2023

Security Knowledge-Guided Fuzzing of Deep Learning Libraries

Recently, many Deep Learning fuzzers have been proposed for testing of DL libraries. However, they either perform unguided input generation (e.g., not considering the relationship between API arguments when generating inputs) or only support a limited set of corner case test inputs. Furthermore, a substantial number of developer APIs crucial for library development remain untested, as they are typically not well-documented and lack clear usage guidelines. To fill this gap, we propose a novel fuzzer named Orion, which combines guided test input generation and corner case test input generation based on a set of fuzzing rules constructed from historical data that is known to trigger vulnerabilities in the implementation of DL APIs. To extract the fuzzing rules, we first conduct an empirical study regarding the root cause analysis of 376 vulnerabilities in two of the most popular DL libraries, i.e., PyTorch and TensorFlow. We then construct the rules based on the root causes of the historical vulnerabilities. Our evaluation shows that Orion reports 135 vulnerabilities on the latest releases of TensorFlow and PyTorch, 76 of which were confirmed by the library developers. Among the 76 confirmed vulnerabilities, 69 are previously unknown, and 7 have already been fixed. The rest are awaiting further confirmation. Regarding end-user APIs, Orion was able to detect 31.8% and 90% more vulnerabilities on TensorFlow and PyTorch, respectively, compared to the state-of-the-art conventional fuzzer, i.e., DeepRel. When compared to the state-of-the-art LLM-based DL fuzzer, AtlasFuzz, Orion detected 13.63% more vulnerabilities on TensorFlow and 18.42% more vulnerabilities on PyTorch. Regarding developer APIs, Orion stands out by detecting 117% more vulnerabilities on TensorFlow and 100% more vulnerabilities on PyTorch compared to the most relevant fuzzer designed for developer APIs, such as FreeFuzz.

cs.CR↗

arXiv2023

SkipAnalyzer: A Tool for Static Code Analysis with Large Language Models

We introduce SkipAnalyzer, a large language model (LLM)-powered tool for static code analysis. SkipAnalyzer has three components: 1) an LLM-based static bug detector that scans source code and reports specific types of bugs, 2) an LLM-based false-positive filter that can identify false-positive bugs in the results of static bug detectors (e.g., the result of step 1) to improve detection accuracy, and 3) an LLM-based patch generator that can generate patches for the detected bugs above. As a proof-of-concept, SkipAnalyzer is built on ChatGPT, which has exhibited outstanding performance in various software engineering tasks. To evaluate SkipAnalyzer, we focus on two types of typical and critical bugs that are targeted by static bug detection, i.e., Null Dereference and Resource Leak as subjects. We employ Infer to aid the gathering of these two bug types from 10 open-source projects. Consequently, our experiment dataset contains 222 instances of Null Dereference bugs and 46 instances of Resource Leak bugs. Our study demonstrates that SkipAnalyzer achieves remarkable performance in the mentioned static analysis tasks, including bug detection, false-positive warning removal, and bug repair. In static bug detection, SkipAnalyzer achieves accuracy values of up to 68.37% for detecting Null Dereference bugs and 76.95% for detecting Resource Leak bugs, improving the precision of the current leading bug detector, Infer, by 12.86% and 43.13%, respectively. For removing false-positive warnings, SkipAnalyzer can reach a precision of up to 93.88% for Null Dereference bugs and 63.33% for Resource Leak bugs. Additionally, SkipAnalyzer surpasses state-of-the-art false-positive warning removal tools. Furthermore, in bug repair, SkipAnalyzer can generate syntactically correct patches to fix its detected bugs with a success rate of up to 97.30%.

cs.SE↗

arXiv2023

Parametric Surface Constrained Upsampler Network for Point Cloud

Designing a point cloud upsampler, which aims to generate a clean and dense point cloud given a sparse point representation, is a fundamental and challenging problem in computer vision. A line of attempts achieves this goal by establishing a point-to-point mapping function via deep neural networks. However, these approaches are prone to produce outlier points due to the lack of explicit surface-level constraints. To solve this problem, we introduce a novel surface regularizer into the upsampler network by forcing the neural network to learn the underlying parametric surface represented by bicubic functions and rotation functions, where the new generated points are then constrained on the underlying surface. These designs are integrated into two different networks for two tasks that take advantages of upsampling layers - point cloud upsampling and point cloud completion for evaluation. The state-of-the-art experimental results on both tasks demonstrate the effectiveness of the proposed method. The code is available at https://github.com/corecai163/PSCU.

cs.CV↗

arXiv2023

The Nearest Neutron Star Candidate in a Binary Revealed by Optical Time-domain Surveys

The near-Earth (within $\sim 100$ pc) supernova explosions in the past several million years can cause the global deposition of radioactive elements (e.g., $^{60}$Fe) on Earth. The remnants of such supernovae are too old to be easily identified. It is therefore of great interest to search for million-year-old near-Earth neutron stars or black holes, the products of supernovae. However, neutron stars and black holes are challenging to find even in our Solar neighbourhood if they are not radio pulsars or X-ray/$γ$-ray emitters. Here we report the discovery of one of the nearest ($127.7 \pm 0.3$ pc) neutron star candidates in a detached single-lined spectroscopic binary LAMOST J235456.73+335625.9 (hereafter J2354). Utilizing the time-resolved ground-based spectroscopy and space photometry, we find that J2354 hosts an unseen compact object with $M_{\mathrm{inv}}$ being $1.4 \sim 1.6\ M_{\odot}$. The follow-up Swift ultraviolet (UV) and X-ray observations suggest that the UV and X-ray emission is produced by the visible star rather than the compact object. Hence, J2354 probably harbours a neutron star rather than a hot ultramassive white dwarf. Two-hour exceptionally sensitive radio follow-up observations with Five-hundred-meter Aperture Spherical radio Telescope fail to reveal any pulsating radio signals at the $6σ$ flux upper limit of $12.5\ μ\mathrm{Jy}$. Therefore, the neutron star candidate in J2354 can only be revealed via our time-resolved observations. Interestingly, the distance between J2354 and our Earth can be as close as $\sim 50$ pc around $2.5$ Myrs ago, as revealed by the Gaia kinematics. Our discovery demonstrates a promising way to unveil the hidden near-Earth neutron stars in binaries by exploring the optical time domain, thereby facilitating understanding of the metal-enrichment history in our Solar neighbourhood.

astro-ph.HE↗

arXiv2023

The Last Decade in Review: Tracing the Evolution of Safety Assurance Cases through a Comprehensive Bibliometric Analysis

Safety assurance is of paramount importance across various domains, including automotive, aerospace, and nuclear energy, where the reliability and acceptability of mission-critical systems are imperative. This assurance is effectively realized through the utilization of Safety Assurance Cases. The use of safety assurance cases allows for verifying the correctness of the created systems capabilities, preventing system failure. The latter may result in loss of life, severe injuries, large-scale environmental damage, property destruction, and major economic loss. Still, the emergence of complex technologies such as cyber-physical systems (CPSs), characterized by their heterogeneity, autonomy, machine learning capabilities, and the uncertainty of their operational environments poses significant challenges for safety assurance activities. Several papers have tried to propose solutions to tackle these challenges, but to the best of our knowledge, no secondary study investigates the trends, patterns, and relationships characterizing the safety case scientific literature. This makes it difficult to have a holistic view of the safety case landscape and to identify the most promising future research directions. In this paper, we, therefore, rely on state-of-the-art bibliometric tools(e.g., VosViewer) to conduct a bibliometric analysis that allows us to generate valuable insights, identify key authors and venues, and gain a birds eye view of the current state of research in the safety assurance area. By revealing knowledge gaps and highlighting potential avenues for future research, our analysis provides an essential foundation for researchers, corporate safety analysts, and regulators seeking to embrace or enhance safety practices that align with their specific needs and objectives.

cs.SE↗

arXiv2023

Interpreting Pretrained Language Models via Concept Bottlenecks

Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. However, the lack of interpretability due to their ``black-box'' nature poses challenges for responsible implementation. Although previous studies have attempted to improve interpretability by using, e.g., attention weights in self-attention layers, these weights often lack clarity, readability, and intuitiveness. In this research, we propose a novel approach to interpreting PLMs by employing high-level, meaningful concepts that are easily understandable for humans. For example, we learn the concept of ``Food'' and investigate how it influences the prediction of a model's sentiment towards a restaurant review. We introduce C$^3$M, which combines human-annotated and machine-generated concepts to extract hidden neurons designed to encapsulate semantically meaningful and task-specific concepts. Through empirical evaluations on real-world datasets, we manifest that our approach offers valuable insights to interpret PLM behavior, helps diagnose model failures, and enhances model robustness amidst noisy concept labels.

cs.CL↗

arXiv2023

Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance

Adopting a two-stage paradigm of pretraining followed by fine-tuning, Pretrained Language Models (PLMs) have achieved substantial advancements in the field of natural language processing. However, in real-world scenarios, data labels are often noisy due to the complex annotation process, making it essential to develop strategies for fine-tuning PLMs with such noisy labels. To this end, we introduce an innovative approach for fine-tuning PLMs using noisy labels, which incorporates the guidance of Large Language Models (LLMs) like ChatGPT. This guidance assists in accurately distinguishing between clean and noisy samples and provides supplementary information beyond the noisy labels, thereby boosting the learning process during fine-tuning PLMs. Extensive experiments on synthetic and real-world noisy datasets further demonstrate the superior advantages of our framework over the state-of-the-art baselines.

cs.CL↗

arXiv2023

Wide binaries with white dwarf or neutron star companions discovered from Gaia DR3 and LAMOST

Gaia DR3 mission has identified and provided about 440,000 binary systems with orbital solutions, offering a valuable resource for searching binaries including a compact component. By combining the Gaia DR3 data with radial velocities (RVs) from the LAMOST spectroscopic survey, we identify three wide binaries possibly containing a compact object. For two of these sources with a main-sequence companion, no obvious excess is observed in the blue/red band of the Gaia DR3 XP spectra, and the LAMOST medium-resolution spectra exhibit clear single-lined features. The absence of an additional component from spectral disentangling analysis further suggests the presence of compact objects within these systems. On the other hand, the visible star of the third source is a stripped giant star. In contrast to most binaries including stripped stars, no emission line is detected in the optical spectra. The unseen star could potentially be a massive white dwarf or neutron star, but the possibility of an F-type dwarf star scenario cannot be ruled out. An examination of about ten binaries containing white dwarfs or neutron stars using both kinematic and chemical methods suggest most of these systems are located in the thin disk of the Milky Way.

astro-ph.SR↗

arXiv2023

Label-efficient Segmentation via Affinity Propagation

Weakly-supervised segmentation with label-efficient sparse annotations has attracted increasing research attention to reduce the cost of laborious pixel-wise labeling process, while the pairwise affinity modeling techniques play an essential role in this task. Most of the existing approaches focus on using the local appearance kernel to model the neighboring pairwise potentials. However, such a local operation fails to capture the long-range dependencies and ignores the topology of objects. In this work, we formulate the affinity modeling as an affinity propagation process, and propose a local and a global pairwise affinity terms to generate accurate soft pseudo labels. An efficient algorithm is also developed to reduce significantly the computational cost. The proposed approach can be conveniently plugged into existing segmentation networks. Experiments on three typical label-efficient segmentation tasks, i.e. box-supervised instance segmentation, point/scribble-supervised semantic segmentation and CLIP-guided semantic segmentation, demonstrate the superior performance of the proposed approach.

cs.CV↗

arXiv2023

ClarifyGPT: Empowering LLM-based Code Generation with Intention Clarification

We introduce a novel framework named ClarifyGPT, which aims to enhance code generation by empowering LLMs with the ability to identify ambiguous requirements and ask targeted clarifying questions. In particular, ClarifyGPT first detects whether a given requirement is ambiguous by performing a code consistency check. If it is ambiguous, ClarifyGPT prompts an LLM to generate targeted clarifying questions. After receiving question responses, ClarifyGPT refines the ambiguous requirement and inputs it into the same LLM to generate a final code solution. To evaluate our ClarifyGPT, we first conduct a human evaluation involving ten participants who use ClarifyGPT for code generation on two publicly available benchmarks: MBPP-sanitized and MBPP-ET. The results show that ClarifyGPT elevates the performance (Pass@1) of GPT-4 from 70.96% to 80.80% on MBPP-sanitized. Furthermore, to perform large-scale automated evaluations of ClarifyGPT across different LLMs and benchmarks without requiring user participation, we introduce a high-fidelity simulation method to simulate user responses. The automated evaluation results also demonstrate that ClarifyGPT can significantly enhance code generation performance compared to the baselines. In particular, ClarifyGPT improves the average performance of GPT-4 and ChatGPT across four benchmarks from 68.02% to 75.75% and from 58.55% to 67.22%, respectively. We believe that ClarifyGPT can effectively facilitate the practical application of LLMs in real-world development environments.

cs.SE↗

arXiv2023

SAIR: Learning Semantic-aware Implicit Representation

Implicit representation of an image can map arbitrary coordinates in the continuous domain to their corresponding color values, presenting a powerful capability for image reconstruction. Nevertheless, existing implicit representation approaches only focus on building continuous appearance mapping, ignoring the continuities of the semantic information across pixels. As a result, they can hardly achieve desired reconstruction results when the semantic information within input images is corrupted, for example, a large region misses. To address the issue, we propose to learn semantic-aware implicit representation (SAIR), that is, we make the implicit representation of each pixel rely on both its appearance and semantic information (\eg, which object does the pixel belong to). To this end, we propose a framework with two modules: (1) building a semantic implicit representation (SIR) for a corrupted image whose large regions miss. Given an arbitrary coordinate in the continuous domain, we can obtain its respective text-aligned embedding indicating the object the pixel belongs. (2) building an appearance implicit representation (AIR) based on the SIR. Given an arbitrary coordinate in the continuous domain, we can reconstruct its color whether or not the pixel is missed in the input. We validate the novel semantic-aware implicit representation method on the image inpainting task, and the extensive experiments demonstrate that our method surpasses state-of-the-art approaches by a significant margin.

cs.CV↗

arXiv2023

CrashTranslator: Automatically Reproducing Mobile Application Crashes Directly from Stack Trace

Crash reports are vital for software maintenance since they allow the developers to be informed of the problems encountered in the mobile application. Before fixing, developers need to reproduce the crash, which is an extremely time-consuming and tedious task. Existing studies conducted the automatic crash reproduction with the natural language described reproducing steps. Yet we find a non-neglectable portion of crash reports only contain the stack trace when the crash occurs. Such stack-trace-only crashes merely reveal the last GUI page when the crash occurs, and lack step-by-step guidance. Developers tend to spend more effort in understanding the problem and reproducing the crash, and existing techniques cannot work on this, thus calling for a greater need for automatic support. This paper proposes an approach named CrashTranslator to automatically reproduce mobile application crashes directly from the stack trace. It accomplishes this by leveraging a pre-trained Large Language Model to predict the exploration steps for triggering the crash, and designing a reinforcement learning based technique to mitigate the inaccurate prediction and guide the search holistically. We evaluate CrashTranslator on 75 crash reports involving 58 popular Android apps, and it successfully reproduces 61.3% of the crashes, outperforming the state-of-the-art baselines by 109% to 206%. Besides, the average reproducing time is 68.7 seconds, outperforming the baselines by 302% to 1611%. We also evaluate the usefulness of CrashTranslator with promising results.

cs.SE↗

arXiv2023

Assessing Evaluation Metrics for Neural Test Oracle Generation

In this work, we revisit existing oracle generation studies plus ChatGPT to empirically investigate the current standing of their performance in both NLG-based and test adequacy metrics. Specifically, we train and run four state-of-the-art test oracle generation models on five NLG-based and two test adequacy metrics for our analysis. We apply two different correlation analyses between these two different sets of metrics. Surprisingly, we found no significant correlation between the NLG-based metrics and test adequacy metrics. For instance, oracles generated from ChatGPT on the project activemq-artemis had the highest performance on all the NLG-based metrics among the studied NOGs, however, it had the most number of projects with a decrease in test adequacy metrics compared to all the studied NOGs. We further conduct a qualitative analysis to explore the reasons behind our observations, we found that oracles with high NLG-based metrics but low test adequacy metrics tend to have complex or multiple chained method invocations within the oracle's parameters, making it hard for the model to generate completely, affecting the test adequacy metrics. On the other hand, oracles with low NLG-based metrics but high test adequacy metrics tend to have to call different assertion types or a different method that functions similarly to the ones in the ground truth. Overall, this work complements prior studies on test oracle generation with an extensive performance evaluation with both NLG and test adequacy metrics and provides guidelines for better assessment of deep learning applications in software test generation in the future.

cs.CL↗