Information and Software Technology最新文献

{"title":"Metamorphic Testing and exploration for Machine Learning credit score models","authors":"Zhihao Ying ,&nbsp;Anthony Graham Bellotti ,&nbsp;Joseph Lynn Breeden ,&nbsp;Dave Towey","doi":"10.1016/j.infsof.2025.107903","DOIUrl":"10.1016/j.infsof.2025.107903","url":null,"abstract":"<div><h3>Context:</h3><div>The rapid development of Machine Learning (ML) has led to the proposal of various ML models to improve credit score assessment, creating a need for effective validation methods to ensure their performance aligns with business expectations.</div></div><div><h3>Objective:</h3><div>This paper introduces a novel approach for validating credit scoring models by focusing on user-hypothesized business expectations, enabling testers to predict how input changes affect outputs and assess alignment with business intuition.</div></div><div><h3>Methods:</h3><div>The approach uses Metamorphic Testing (MT), applying Metamorphic Relations (MRs) to examine input–output relationships, and Metamorphic Exploration (ME), an advanced extension of MT that constructs MRs based on user expectations. A case study evaluates and contrasts three popular ML models, neural networks, random forests, and gradient boosting tree, using both traditional evaluation metrics in credit scoring and ME. The study investigates how models selected based on traditional metrics perform when evaluated against MRs.</div></div><div><h3>Results:</h3><div>Empirical findings reveal that all three models often violate MRs, with violations becoming more extensive as model complexity increases. Neural networks have low number of MR violations on average but tends to be less robust. Interestingly, random forests exhibit most MR violations relative to the other two models. Traditional metrics fail to capture these violations, highlighting their limitations in ensuring alignment with business expectations.</div></div><div><h3>Conclusions:</h3><div>ME is proposed as a complementary validation method for model selection and post-deployment monitoring, ensuring models adhere to business intuition. The study underscores the importance of combining traditional metrics with ME, particularly for complex models like neural networks, to improve reliability in real-world applications.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"188 ","pages":"Article 107903"},"PeriodicalIF":4.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ProSpec: Profile-guided Specialization for GPU Kernels","authors":"Jiali Liang,&nbsp;Lin Chen,&nbsp;Xiangyu Zhang,&nbsp;Yanhui Li,&nbsp;Yuming Zhou","doi":"10.1016/j.infsof.2025.107901","DOIUrl":"10.1016/j.infsof.2025.107901","url":null,"abstract":"<div><div>General-purpose GPUs are widely used for computational acceleration in various fields. Designing high-performance GPU kernels is challenging due to dynamic kernel variables and complex GPU architectures.</div><div>Leveraging runtime profiling to identify value-related inefficiencies is effective for optimizing GPU kernels, but it faces several challenges: (1) high profiling overhead, (2) limited analysis of inter-variable correlations, and (3) lack of automated optimization mechanisms.</div><div>In this paper, we propose a profile-guided optimization technique named ProSpec for GPU Kernel Specialization. It offloads profile collection to CPUs, analyzes inefficiency patterns dependent on multiple hot values, and generates optimization feedback for automatic kernel specialization.</div><div>The prototype of ProSpec, implemented over the LLVM infrastructure, is evaluated on the Rodinia and Polybench benchmarks. It achieves a maximum speedup of 5.619x and an average of 1.417x on optimized applications, maintaining a low profiling overhead of around 1.01x.</div><div>Compared to state-of-the-art methods, ProSpec leads in the number of improved kernels and further optimizes half of those already optimized by other tools.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"188 ","pages":"Article 107901"},"PeriodicalIF":4.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whose voices are heard? Gender disparities in platform-facilitated discrimination and content moderation","authors":"Lauren Olson ,&nbsp;Ricarda Anna-Lena Fischer ,&nbsp;Tom P. Humbert ,&nbsp;Florian Kunneman ,&nbsp;Emitzá Guzmán","doi":"10.1016/j.infsof.2025.107899","DOIUrl":"10.1016/j.infsof.2025.107899","url":null,"abstract":"<div><h3>Context:</h3><div>Historically, cisgender men have maintained systemic social, cultural, and political privilege over other genders. Online discrimination serves as a mechanism for reinforcing this dominance. Content moderation plays a crucial role in shaping online experiences, yet the ways it may perpetuate or mitigate discrimination remain underexplored.</div></div><div><h3>Objective:</h3><div>This study examines how content moderation and discussions of discrimination vary across gendered online communities, with a focus on identifying differential impacts by gender group.</div></div><div><h3>Methods:</h3><div>We analyzed 124 subreddits spanning three gender groups—women, men, and gender minorities (GM). The analysis included manual annotation of 1,535 posts and machine learning classification of an additional 6,613 posts to assess the prevalence of user discussions regarding online discrimination and content moderation.</div></div><div><h3>Results:</h3><div>Women were most likely to report top-down moderation issues, such as bans and content removal, while GM users engaged more frequently with general moderation concerns. Time series analysis revealed that complaints about content moderation have increased over time, with the steepest rise among women users. These patterns demonstrate that moderation policies and enforcement impact gender groups differently.</div></div><div><h3>Conclusion:</h3><div>Our findings highlight the need for improvements in software engineering and user experience design for content moderation tools. Enhancing transparency, promoting equity, and enabling more user-driven moderation experiences are critical steps towards protecting marginalized groups against discrimination online.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"188 ","pages":"Article 107899"},"PeriodicalIF":4.3,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards imbalanced regression over distributionally biased data: A fast static approach","authors":"Wentai Wu ,&nbsp;Ligang He ,&nbsp;Weiwei Lin ,&nbsp;Jinyi Long ,&nbsp;Zhiquan Liu ,&nbsp;C.L. Philip Chen","doi":"10.1016/j.infsof.2025.107897","DOIUrl":"10.1016/j.infsof.2025.107897","url":null,"abstract":"<div><div>The generalization of models are susceptible to data bias for both classification and regression problems, which also has intrinsic connection to the issues of fairness. However, existing approaches focus on class-imbalanced learning and fail to address these concerns for regressors. In this paper, we target at imbalanced regression with particular focus on fusing distributional information from both the feature space and target space. We first introduce two metrics, uniqueness and abnormality, to reflect local data distribution and assess the informativeness of each sample from a regional perspective in the two spaces. By integrating these two metrics we propose a local Variation-incented re-weighting method, termed <span>ViLoss</span>, which fuses distributional information for each sample to optimize gradient-based regressor training. The weights are computed once-and-for-all in pre-processing and thus our method causes little extra computation during training. Empirically, we conducted comprehensive experiments on both synthetic and real-world datasets for parameter study and performance evaluation. The results demonstrate the efficacy of our method in boosting model quality (error reduction by up to 39.0%) as well as narrowing the gap of error between groups.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"188 ","pages":"Article 107897"},"PeriodicalIF":4.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metamorphic Testing of Multimodal Human Trajectory Prediction","authors":"Helge Spieker ,&nbsp;Nadjib Lazaar ,&nbsp;Arnaud Gotlieb ,&nbsp;Nassim Belmecheri","doi":"10.1016/j.infsof.2025.107890","DOIUrl":"10.1016/j.infsof.2025.107890","url":null,"abstract":"<div><h3>Context:</h3><div>Predicting human trajectories is crucial for the safety and reliability of autonomous systems, such as automated vehicles and mobile robots. However, rigorously testing the underlying multimodal Human Trajectory Prediction (HTP) models, which typically use multiple input sources (e.g., trajectory history and environment maps) and produce stochastic outputs (multiple possible future paths), presents significant challenges. The primary difficulty lies in the absence of a definitive test oracle, as numerous future trajectories might be plausible for any given scenario.</div></div><div><h3>Objectives:</h3><div>This research presents the application of Metamorphic Testing (MT) as a systematic methodology for testing multimodal HTP systems. We address the oracle problem through metamorphic relations (MRs) adapted for the complexities and stochastic nature of HTP.</div></div><div><h3>Methods:</h3><div>We present five MRs, targeting transformations of both historical trajectory data and semantic segmentation maps used as an environmental context. These MRs encompass: (1) label-preserving geometric transformations (mirroring, rotation, rescaling) applied to both trajectory and map inputs, where outputs are expected to transform correspondingly. (2) Map-altering transformations (changing semantic class labels, introducing obstacles) with predictable changes in trajectory distributions. We propose probabilistic violation criteria based on distance metrics between probability distributions, such as the Wasserstein or Hellinger distance.</div></div><div><h3>Results:</h3><div>The empirical evaluation on a popular HTP model called Y-net demonstrated the feasibility and effectiveness of TrajTest on this dataset. For label-preserving MRs, the oracle-less Wasserstein violation criterion identified violations with statistically significant agreement relative to ground-truth-dependent metrics, confirming its utility. Map-altering MRs successfully triggered expected changes, such as statistically significant decreases in path probabilities over areas made less walkable or obstacle avoidance.</div></div><div><h3>Conclusion:</h3><div>This study introduces TrajTest, a MT framework for the oracle-less testing of multimodal, stochastic HTP systems. It allows for assessment of model robustness against input transformations and contextual changes without reliance on ground-truth trajectories.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"188 ","pages":"Article 107890"},"PeriodicalIF":4.3,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy-fairness trade-off in ML for healthcare: A quantitative evaluation of bias mitigation strategies","authors":"Farzaneh Dehghani ,&nbsp;Pedro Paiva ,&nbsp;Nikita Malik ,&nbsp;Joanna Lin ,&nbsp;Sayeh Bayat ,&nbsp;Mariana Bento","doi":"10.1016/j.infsof.2025.107896","DOIUrl":"10.1016/j.infsof.2025.107896","url":null,"abstract":"<div><h3>Context:</h3><div>Although machine learning (ML) has significant potential to improve healthcare decision-making, embedded biases in algorithms and datasets risk exacerbating health disparities across demographic groups. To address this challenge, it is essential to rigorously evaluate bias mitigation strategies to ensure fairness and reliability across patient populations.</div></div><div><h3>Objective:</h3><div>The aim of this research is to propose a comprehensive evaluation framework that systematically assesses a wide range of bias mitigation techniques at pre-processing, in-processing, and post-processing stages, using both single- and multi-stage intervention approaches.</div></div><div><h3>Methods:</h3><div>This study evaluates bias mitigation strategies across three clinical prediction tasks: breast cancer diagnosis, stroke prediction, and Alzheimer’s disease detection. Our evaluation employs group- and individual-level fairness metrics, contextualized for specific sensitive attributes relevant to each dataset. Beyond fairness-accuracy trade-offs, we demonstrate how metric selection must align with clinical goals (e.g., parity metrics for equitable access, confusion-matrix metrics for diagnostics).</div></div><div><h3>Results:</h3><div>Our results reinforce that no single classifier or mitigation strategy is universally optimal, underscoring the value of our proposed framework for evaluating fairness and accuracy throughout the bias mitigation process. According to the results, Adversarial Debiasing improved fairness by 95% in breast cancer diagnosis without compromising accuracy. Reweighing was most effective in stroke prediction, boosting fairness by 41%, and Reject Option Classification yielded nearly 50% fairness improvement in Alzheimer’s detection. Multi-stage bias mitigation did not consistently lead to better outcomes, and in many cases, fairness gains came at the expense of accuracy.</div></div><div><h3>Conclusion:</h3><div>These findings provide practical guidance for selecting fairness-aware machine learning strategies in healthcare, aiding both model development and benchmarking across diverse clinical applications.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"188 ","pages":"Article 107896"},"PeriodicalIF":4.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Directed fuzzing based on path constraints and deviation path correction","authors":"Hongsheng Zuo,&nbsp;Yong Fang,&nbsp;Peng Jia,&nbsp;Ximing Fan,&nbsp;Xi Peng,&nbsp;YiJia Xu,&nbsp;Rui Pan","doi":"10.1016/j.infsof.2025.107875","DOIUrl":"10.1016/j.infsof.2025.107875","url":null,"abstract":"<div><div>Directed fuzzing targets specific parts of a program and is particularly useful for tasks like PoC verification, crash reproduction, and patch testing. It uses static analysis to guide the fuzzing process. However, it still faces two significant challenges that affect its efficiency. They overlook whether the code region is related to the target location, causing excessive computational power to be wasted on calculating distances for irrelevant regions and hindering precise distance calculation. Additionally, the reliance on random mutations during test case generation results in new cases that are mostly unreachable to the target location. Therefore, we propose PathFuzz, a fuzzer that employs a distance calculation method constrained by the target path and a directed mutation method for the bytes that cause deviation from the target path. We first identified code regions related to the target location and calculated finer-grained basic block distances within these regions. Next, through taint analysis, we map program input bytes to the basic blocks that process them. We then perform directed mutations, adjusting the input bytes corresponding to the basic blocks that deviate from the target execution path to guide it back on track. We evaluated PathFuzz on Magma dataset, and experiments show that compared to several SOTA directed fuzzers, PathFuzz is on average 17.85 times, 2.47 times, 17.61 times, and 4.85 times faster, respectively, and it can trigger four vulnerabilities that other tools cannot within the specified time.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"188 ","pages":"Article 107875"},"PeriodicalIF":4.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VulTriNet: A software vulnerability detection method based on tri-channel network","authors":"Yiyao Yang,&nbsp;Youjian Yao,&nbsp;Xiao Lv,&nbsp;Wen Chen","doi":"10.1016/j.infsof.2025.107893","DOIUrl":"10.1016/j.infsof.2025.107893","url":null,"abstract":"<div><h3>Context:</h3><div>Software vulnerabilities represent a critical concern in cybersecurity. As vulnerability patterns become increasingly complex, advanced detection methods are needed to fully analyze them. Recent studies have treated source codes as text using natural language processing (NLP) techniques. Subsequent advancements transformed programs into intermediate representations, utilizing graph neural network (GNN) for vulnerability learning. However, these approaches exhibit limitations in software vulnerability detection, as they fail to comprehensively analyze the features of source code.</div></div><div><h3>Objective:</h3><div>To solve this problem, we proposed a novel vulnerability detection method based on a tri-channel network (VulTriNet), which enables comprehensive analysis of source code and effective vulnerability detection.</div></div><div><h3>Methods:</h3><div>The Method integrates two graph-based and one textual code representation using three distinct methods to transform functions into multiple forms. Then, inspired by the RGB three-channel concept in the image domain, VulTriNet generates corresponding embedding vectors for these transformed representations, which are subsequently merged into a unified three-channel feature matrix. Finally, there is a CNN model integrated with attention mechanisms to improve the capability of detecting vulnerabilities.</div></div><div><h3>Results:</h3><div>Experimental results demonstrated that, compared to five state-of-the-art approaches, VulTriNet achieves, on average across different datasets: a 4.89% improvement in accuracy, a 3.41% increase in TNR, a 4.09% gain in TPR, and a 4.18% boost in F1-score.</div></div><div><h3>Conclusion:</h3><div>These results indicate that VulTriNet is more accurate and effective than previous studies. This hybrid analysis model strengthens vulnerability detection capabilities by simultaneously preserving contextual understanding of code and awareness of its structural relationships.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"188 ","pages":"Article 107893"},"PeriodicalIF":4.3,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating similarity-based model matching with subtree equivalence","authors":"Xiao He ,&nbsp;Kai Liu ,&nbsp;Yifan Zhang ,&nbsp;Huihong He","doi":"10.1016/j.infsof.2025.107879","DOIUrl":"10.1016/j.infsof.2025.107879","url":null,"abstract":"<div><h3>Context:</h3><div>Efficient version management of models in model-driven software engineering is vital for modeling tools, necessitating model matching, differencing, and merging to incorporate various model versions. Although similarity-based matching is the most general method, its computational complexity escalates at a cubic rate with the number of elements.</div></div><div><h3>Objective:</h3><div>This paper introduces <span>StEqMatch</span>, a subtree-equivalence-based approach to accelerate similarity model matching, inspired by the observation that consecutive version changes typically impact only a small portion of a model.</div></div><div><h3>Methods:</h3><div><span>StEqMatch</span> initially decomposes a model into a series of subtrees. Rather than performing element-wise matching directly, our approach tries to find equivalent (i.e., either identical or closely similar) subtrees, representing the unchanged portion of a model, thus enabling quick pairing of elements within these subtrees. To effectively identify equivalent subtrees, this paper develops two hash functions for equality and similarity comparison of model trees.</div></div><div><h3>Results:</h3><div>Experiments using open-source Ecore and UML models indicate that <span>StEqMatch</span> is 1.27 to 22.5 times faster on average compared to the state-of-the-art model matching tool while reducing the error rates in most cases.</div></div><div><h3>Conclusion:</h3><div><span>StEqMatch</span> combines subtree matching and element-wise matching, and can improve the efficiency and the quality of similarity-based model matching.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"188 ","pages":"Article 107879"},"PeriodicalIF":4.3,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic literature review on distributed ledger technologies in healthcare: Applications, challenges, and future directions","authors":"Fatemeh Esmaeilnezhadtanha ,&nbsp;Luca Spalazzi ,&nbsp;Luca Del Bene ,&nbsp;Yves Wautelet","doi":"10.1016/j.infsof.2025.107894","DOIUrl":"10.1016/j.infsof.2025.107894","url":null,"abstract":"<div><h3>Context</h3><div>Distributed Ledger Technologies (DLTs), including blockchain, are increasingly used as tools enabling the transformation and improvement of healthcare management. There is a strong need to study how these technologies have been impacting healthcare recently through a review of the scientific literature on the subject.</div></div><div><h3>Objective</h3><div>This study explores how, as reported in the literature, DLTs can enhance healthcare data and process management with or without complementary technologies like AI, IoT, and cloud computing.</div></div><div><h3>Method</h3><div>Through a systematic literature review, the paper analyzes the contributions of DLTs to healthcare and proposes a reference conceptual model for their adoption, with a focus on affordability and systemic enhancements.</div></div><div><h3>Results</h3><div>The study highlights challenges such as scalability, privacy, and regulatory compliance; it also shows innovative opportunities such as real-time monitoring and secure data exchange. Literature identifies that the integration of DLTs with other emerging technologies is transforming healthcare practices. More work should be done at the level of Layer 2 solutions to improve scalability while preserving security and decentralization.</div></div><div><h3>Conclusion</h3><div>To fully unlock DLT’s potential and overcome current challenges, collaboration among healthcare stakeholders and policymakers will be essential. The reference model offers a strategic framework for healthcare providers, outlining key directions for future research and development to enhance healthcare services and operational effectiveness.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"188 ","pages":"Article 107894"},"PeriodicalIF":4.3,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}