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Wait, wasn’t that code here before? Detecting Outdated Software Documentation

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arXiv 2023年

作者： Tan, Wen Siang Wagner, Markus Treude, Christoph School of Computer Science University of Adelaide AdelaideSA Australia Department of Data Science AI Monash University MelbourneVIC Australia School of Computing and Information Systems The University of Melbourne MelbourneVIC Australia

Encountering outdated documentation is not a rare occurrence for developers and users in the software engineering community. To ensure that software documentation is up-to-date, developers often have to manually check whether the documentation needs to be updated whenever changes are made to the source code. In our previous work, we proposed an approach to automatically detect outdated code element references in software repositories and found that more than a quarter of the 1000 most popular projects on GitHub contained at least one outdated reference. In this paper, we present a GitHub Actions tool that builds on our previous work’s approach that GitHub developers can configure to automatically scan for outdated code element references in their GitHub project’s documentation whenever a pull request is submitted. Copyright © 2023, The Authors. All rights reserved.

关键词： Software engineering

Towards Adversarially Robust dataset Distillation by Curvature Regularization

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arXiv 2024年

作者： Xue, Eric Li, Yijiang Liu, Haoyang Wang, Peiran Shen, Yifan Wang, Haohan Department of Computer Science University of Toronto Canada University of California San Diego United States School of Information Sciences University of Illinois Urbana-Champaign United States Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States

dataset distillation (DD) allows datasets to be distilled to fractions of their original size while preserving the rich distributional information so that models trained on the distilled datasets can achieve a comparable accuracy while saving significant computational loads. Recent research in this area has been focusing on improving the accuracy of models trained on distilled datasets. In this paper, we aim to explore a new perspective of DD. We study how to embed adversarial robustness in distilled datasets, so that models trained on these datasets maintain the high accuracy and meanwhile acquire better adversarial robustness. We propose a new method that achieves this goal by incorporating curvature regularization into the distillation process with much less computational overhead than standard adversarial training. Extensive empirical experiments suggest that our method not only outperforms standard adversarial training on both accuracy and robustness with less computation overhead but is also capable of generating robust distilled datasets that can withstand various adversarial attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

Mixup data Augmentation and Channel-Spatial Attention Mechanisms for Facial Emotion Recognition

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Mixup Data Augmentation and Channel-Spatial Attention Mechan...

IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Dewei Yu Chuijian Kong Xianpiao Tang Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Guiyang China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal Universitya Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the rapid advancement of computer vision, facial expression recognition has made significant progress in areas like human-computer interaction, mental health, driver assistance, and education. However, traditional facial expression recognition techniques exhibit poor robustness under complex backgrounds and varying lighting conditions, making them susceptible to environmental noise and challenging to accurately capture and classify complex emotional states. Additionally, these techniques often lack in data augmentation and model generalization capabilities, leading to overfitting on training samples and insufficient recognition of unseen samples. To address these limitations, this paper proposes a novel convolutional neural network model—HSCNet (A Hybrid data Augmentation and Channel-Spatial Attention Mechanisms Neural Network). The HSCNet model first employs the Mixup data augmentation technique to generate new training samples, then utilizes the Res2Net50 backbone network combined with the CBAM attention mechanism and average-max pooling techniques to extract significant features while suppressing redundant ones. This approach not only enhances feature extraction accuracy but also significantly improves the model’s generalization ability and robustness in facial expression recognition tasks. Finally, tested on the public dataset FERPlus, which includes eight types of emotions, the HSCNet model achieved an accuracy rate of 89.72%, demonstrating excellent performance and robustness.

关键词： Training Emotion recognition Attention mechanisms Accuracy Face recognition Lighting Feature extraction data augmentation data models Robustness

Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

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computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara Zivkovic Miodrag Zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia Informatics and Computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia Computing and Informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and Informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of Electrical and Electronics Engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： Coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

Resolution-Adaptive Representation Learning via Eliminating Abnormal Features for Cross-Resolution Person Re-Identification

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Resolution-Adaptive Representation Learning via Eliminating ...

IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Xing Lu Fengshan Lai Zhixiang Cao Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Guiyang China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Cross-resolution person re-identification(CR-ReID) seeks to overcome the challenge of retrieving and matching specific person images across cameras with varying resolutions. Numerous existing studies utilize established CNNs and ViTs models to resize captured low-resolution (LR) images and align them with high-resolution (HR) image features. However, these methods overlook the potential feature connections between LR and HR images. Consequently, CNNs or ViTs tend to highlight outliers rather than the intended characteristics in LR image attention maps. In this work, we propose the abnormal feature elimination and reconfiguration transformer (ART) network, a novel network architecture, for robust cross-resolution person re-identification tasks. Our proposed method uses a resolution feature discriminator to learn resolution invariant features and outputs feature matrices of images with different resolutions. We utilize output feature matrices to model LR and HR image interactions through cross-resolution landmark agent attention. We aim to mitigate abnormal image features and prioritize the attention on the target person by learning representations from input images of various resolutions. Extensive evaluations on three real-world datasets confirm the excellent results of our approach.

关键词： Representation learning Image resolution Pedestrians Computational modeling Subspace constraints Network architecture Transformers Hardware information technology Identification of persons

Semi-supervised meibomian gland segmentation via mutual consistency constraints and uncertainty rectification

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Semi-supervised meibomian gland segmentation via mutual cons...

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lin, Jiawen Lu, Feng Li, Li Lin, Lingjie Li, Dongqi Borchert, Glen M. Huang, Jingshan Fuzhou University College of Computer and Data Science Fuzhou China Fujian Provincial Hospital South Branch Department of Ophthalmology Fuzhou China University of California Irvine Department of Economics School of Information and Computer Sciences Irvine United States University of South Alabama College of Medicine Department of Pharmacology Mobile United States University of South Alabama School of Computing & College of Medicine Mobile United States

ISBN: (纸本)9798350337488

To solve the label scarcity of meibomian gland segmentation in infrared meibography images, a novel framework for semi-supervised meibomian gland segmentation is firstly presented in this paper. Extra mutual feature consistency constraint is added along with the cross pseudo supervision , guiding the model more robustness and discriminative. Meanwhile, cross uncertainty rectification is introduced to avoid noisy labels, further improving the pseudo supervision. Experimental results on an internal dataset reveals that our method yields significant performances using only 10% of the labeled data compared to the fully supervised segmentation, and outperforms the state-of-the art semi-supervised segmentation methods. Combination of mutual consistency regularization and cross uncertainty rectifi-cation guides model to distinguish glands from background well with limited labeled data. © 2023 IEEE.

关键词： meibomian gland dysfunction meibomian gland segmentation mutual consistency constraint semi-supervised learning uncertainty rectification

Variable projection algorithms with sparse constraint for separable nonlinear models

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Control Theory and Technology 2024年第1期22卷 135-146页

作者： Hui-Lang Xu Guang-Yong Chen Si-Qing Cheng Min Gan Jing Chen College of Computer and Data Science Fuzhou UniversityFuzhou350116FujianChina Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350116FujianChina Key Laboratory of Intelligent Metro of Universities in Fujian Fuzhou UniversityFuzhou350116FujianChina Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou UniversityFuzhou350116FujianChina School of Science Jiangnan UniversityWuxi214122JiangsuChina

Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. However, identifying the parameters of these models is challenging, especially when sparse models with better interpretability are desired by practitioners. Previous theoretical and practical studies have shown that variable projection (VP) is an efficient method for identifying separable nonlinear models, but these are based on \(L_2\) penalty of model parameters, which cannot be directly extended to deal with sparse constraint. Based on the exploration of the structural characteristics of separable models, this paper proposes gradient-based and trust-region-based variable projection algorithms, which mainly solve two key problems: how to eliminate linear parameters under sparse constraint;and how to deal with the coupling relationship between linear and nonlinear parameters in the model. Finally, numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.

关键词： Variable projection(VP) Non-smooth constraint Separable nonlinear models

Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework

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arXiv 2023年

作者： Wang, Shiping Wu, Zhihao Chen, Yuhong Chen, Yong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China School of Computer Science Beijing University of Posts and Telecommunications China

Graph convolutional networks (GCNs) have been attracting widespread attentions due to their encouraging performance and powerful generalizations. However, few work provide a general view to interpret various GCNs and guide GCNs’ designs. In this paper, by revisiting the original GCN, we induce an interpretable regularizer-centerd optimization framework, in which by building appropriate regularizers we can interpret most GCNs, such as APPNP, JKNet, DAGNN, and GNN-LF/HF. Further, under the proposed framework, we devise a dual-regularizer graph convolutional network (dubbed tsGCN) to capture topological and semantic structures from graph data. Since the derived learning rule for tsGCN contains an inverse of a large matrix and thus is time-consuming, we leverage the Woodbury matrix identity and low-rank approximation tricks to successfully decrease the high computational complexity of computing infinite-order graph convolutions. Extensive experiments on eight public datasets demonstrate that tsGCN achieves superior performance against quite a few state-of-the-art competitors w.r.t. classification *** Codes 68T07 Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

VULSEYE: Detect Smart Contract Vulnerabilities via Stateful Directed Graybox Fuzzing

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arXiv 2024年

作者： Liang, Ruichao Chen, Jing Wu, Cong He, Kun Wu, Yueming Cao, Ruochen Du, Ruiying Liu, Yang Zhao, Ziming Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China Cyber Security Lab College of Computing and Data Science Nanyang Technological University Singapore Department of Computer Science and Engineering University at Buffalo New York United States

Smart contracts, the cornerstone of decentralized applications, have become increasingly prominent in revolutionizing the digital landscape. However, vulnerabilities in smart contracts pose great risks to user assets and undermine overall trust in decentralized systems. Fuzzing, a prominent security testing technique, is extensively explored to detect vulnerabilities. But current smart contract fuzzers fall short of expectations in testing efficiency for two primary reasons. Firstly, smart contracts are stateful programs, and existing approaches, primarily coverage-guided, lack effective feedback from the contract state. Consequently, they struggle to effectively explore the contract state space. Secondly, coverage-guided fuzzers, aiming for comprehensive program coverage, may lead to a wastage of testing resources on benign code areas. This wastage worsens in smart contract testing, as the mix of code and state spaces further complicates comprehensive testing. To address these challenges, we propose VULSEYE, a stateful directed graybox fuzzer for smart contracts guided by vulnerabilities. Different from prior works, VULSEYE achieves stateful directed fuzzing by prioritizing testing resources to code areas and contract states that are more prone to vulnerabilities. We introduce Code Targets and State Targets into fuzzing loops as the testing targets of VULSEYE. We use static analysis and pattern matching to pinpoint Code Targets, and propose a scalable backward analysis algorithm to specify State Targets. We design a novel fitness metric that leverages feedback from both the contract code space and state space, directing fuzzing toward these targets. With the guidance of code and state targets, VULSEYE alleviates the wastage of testing resources on benign code areas and achieves effective stateful fuzzing. In comparison with state-of-the-art fuzzers, VULSEYE demonstrated superior effectiveness and efficiency. Notably, it uncovered 4,845 vulnerabilities in 42,738 rea

关键词： Software testing