Knowledge-Based Systems最新文献_第9页

KnowBug: Enhancing Large language models with bug report knowledge for deep learning framework bug prediction KnowBug：利用错误报告知识增强大型语言模型，用于深度学习框架的错误预测

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-10 DOI: 10.1016/j.knosys.2024.112588

Chenglong Li , Zheng Zheng , Xiaoting Du , Xiangyue Ma , Zhengqi Wang , Xinheng Li

引用次数: 0

Differential evolution with ring sub-population architecture for optimization 采用环状子群结构的差分进化优化技术

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-10 DOI: 10.1016/j.knosys.2024.112590

Zhen Li , Kaiyu Wang , Chenxi Xue , Haotian Li , Yuki Todo , Zhenyu Lei , Shangce Gao

{"title":"Differential evolution with ring sub-population architecture for optimization","authors":"Zhen Li , Kaiyu Wang , Chenxi Xue , Haotian Li , Yuki Todo , Zhenyu Lei , Shangce Gao","doi":"10.1016/j.knosys.2024.112590","DOIUrl":"10.1016/j.knosys.2024.112590","url":null,"abstract":"<div><div>In recent years, evolutionary algorithms have achieved outstanding results in addressing increasingly complex optimization problems, with differential evolution (DE) gaining significant attention. However, due to its simple yet efficient evolutionary mechanism, DE has consistently faced challenges in mitigating the risk of premature convergence. This paper introduces a novel Ring Sub-population architecture-based Differential Evolution (RSDE) to address this issue. RSDE incorporates a conditional similarity selection mechanism that integrates multiple strategies. By considering fitness evaluation and population distribution, RSDE facilitates rich information exchange among sub-populations, leading to cyclic optimization. This global conditional interaction mechanism provides a new idea for population structure research, effectively preserves valuable solutions within the population, and prevents stagnation due to rapid convergence. The performance of RSDE is rigorously evaluated using 29 benchmark functions from the IEEE Congress on Evolutionary Computation (CEC) 2017, 22 real-world problems from CEC2011, and 12 complex optimization problems from CEC2022. RSDE is compared with 18 advanced algorithms, including leading DE variants and other state-of-the-art methods. The results demonstrate that the proposed RSDE algorithm performs well and is highly competitive with other competitors.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid federated kernel regularized least squares algorithm 混合联合内核正则化最小二乘法算法

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-10 DOI: 10.1016/j.knosys.2024.112600

Celeste Damiani , Yulia Rodina , Sergio Decherchi

引用次数: 0

Vision-and-language navigation based on history-aware cross-modal feature fusion in indoor environment 基于历史感知跨模态特征融合的室内环境视觉语言导航

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-10 DOI: 10.1016/j.knosys.2024.112610

Shuhuan Wen , Simeng Gong , Ziyuan Zhang , F. Richard Yu , Zhiwen Wang

{"title":"Vision-and-language navigation based on history-aware cross-modal feature fusion in indoor environment","authors":"Shuhuan Wen , Simeng Gong , Ziyuan Zhang , F. Richard Yu , Zhiwen Wang","doi":"10.1016/j.knosys.2024.112610","DOIUrl":"10.1016/j.knosys.2024.112610","url":null,"abstract":"<div><div>Vision-and-language navigation (VLN) is a challenging task that requires an agent to navigate an indoor environment using natural language instructions. Traditional VLN employs cross-modal feature fusion, where visual and textual information are combined to guide the agent’s navigation. However, incomplete use of perceptual information, scarcity of domain-specific training data, and diverse image and language inputs result in suboptimal performance. Herein, we propose a cross-modal feature fusion VLN history-aware information, that leverages an agent’s past experiences to make more informed navigation decisions. The regretful model and self-monitoring models are added, and the advantage actor critic(A2C) reinforcement learning algorithm is employed to improve the navigation success rate, reduce action redundancy, and shorten navigation paths. Subsequently, a data augmentation method based on speaker data is introduced to improve the model generalizability. We evaluate the proposed algorithm on the room-to-room (R2R) and room-for-room (R4R) benchmarks, and the experimental results demonstrate that, by comparison, the proposed algorithm outperforms state-of-the-art methods.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supervised kernel principal component analysis-polynomial chaos-Kriging for high-dimensional surrogate modelling and optimization 用于高维代用建模和优化的有监督内核主成分分析-多项式混沌-克里金法

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-09 DOI: 10.1016/j.knosys.2024.112617

Huan Zhao , Zhiyuan Gong , Keyao Gan , Yujie Gan , Haonan Xing , Shekun Wang

{"title":"Supervised kernel principal component analysis-polynomial chaos-Kriging for high-dimensional surrogate modelling and optimization","authors":"Huan Zhao , Zhiyuan Gong , Keyao Gan , Yujie Gan , Haonan Xing , Shekun Wang","doi":"10.1016/j.knosys.2024.112617","DOIUrl":"10.1016/j.knosys.2024.112617","url":null,"abstract":"<div><div>Surrogate-based optimization (SBO) approach is becoming more and more popular in the expensive aerodynamic design of aircraft. However, with increasing number of design variables required for parameterizing a complex shape, SBO is suffering from the serious difficulty of the curse of dimensionality. To ameliorate this issue, a supervised nonlinear dimensionality-reduction surrogate modelling method was proposed. Such a method combines the supervised kernel principal component analysis (SKPCA) and polynomial chaos-Kriging (PCK) techniques into the jointly surrogate modelling process and adaptively establishes the accurate mapping from the high-dimensional inputs to the output of the system. This SKPCA-PCK method, which fully considers the effect of inputs on outputs and adaptively trains these hyper-parameters in the surrogate modelling process, escapes from the low prediction accuracy and instability of the surrogate model in conjunction with current linear or unsupervised dimensionality-reduction methods. Further, an efficient SKPCA-PCK-based global optimization method for high-dimensional aerodynamic design was developed. The performance of the proposed method is examined by investigating two numerical examples, the transonic RAE2822 airfoil and the wing of the NASA Common Research Model. Results demonstrate that the proposed SKPCA-PCK method significantly improves the modelling efficiency and accuracy compared to the unsupervised linear PCA-Kriging method. More importantly, the proposed SKPCA-PCK-based optimization method provides better performance and an appreciably higher optimization efficiency for expensive single-point and robust aerodynamic design involving high-dimensional design variables compared to the Kriging-based optimization method. These results provide further evidence that the proposed method provides a promising approach for mitigating the curse of dimensionality in SBO.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contrastive clustering based on generalized bias-variance decomposition 基于广义偏差-方差分解的对比聚类法

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-09 DOI: 10.1016/j.knosys.2024.112601

Shu Li , Lixin Han , Yang Wang , Yonglin Pu , Jun Zhu , Jingxian Li

{"title":"Contrastive clustering based on generalized bias-variance decomposition","authors":"Shu Li , Lixin Han , Yang Wang , Yonglin Pu , Jun Zhu , Jingxian Li","doi":"10.1016/j.knosys.2024.112601","DOIUrl":"10.1016/j.knosys.2024.112601","url":null,"abstract":"<div><div>Contrastive learning demonstrates remarkable generalization performance but lacks theoretical understanding, while contrastive clustering achieves promising performance but exhibits some shortcomings. We first introduce a generalized bias-variance decomposition to study contrastive learning, then present the concept of the conformal field, which unifies instance-level contrastive loss and cluster-level de-redundancy loss (Barlow Twins). Finally, we integrate the conformal field and self-labeling to propose the outstanding contrastive clustering model D3CF. D3CF consists of two novel stages: the pre-training stage simultaneously performs instance-level contrastive learning and multi-view cluster-level redundancy reduction, bringing positive samples together and separating negative samples in the row and column space of the augmented feature matrix; to alleviate the adverse effects caused by false-negative pairs and misclustered assignments in the pre-training stage, the boosting stage enhances contrastive learning from single-positive pairs to multiple-positive pairs by leveraging cross-sample similarities, while utilizing pseudo-labels with high confidence criteria for self-labeling to correct clustering assignments. Extensive experiments on six image benchmark datasets and two text benchmarks demonstrate D3CF’s superior performance and validate the effectiveness of its components. Particularly on CIFAR-10, ImageNet-10, and STL-10, D3CF achieves average accuracies of 89.5%, 97%, and 91%, improving NMI by 5.2%, 4.8%, and 2.1%, and ARI by 7%, 7.3%, and 7.3% over the closest baseline.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gradient-guided channel masking for cross-domain few-shot learning 梯度引导的通道掩蔽，用于跨域少量学习

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-09 DOI: 10.1016/j.knosys.2024.112548

Siqi Hui , Sanping Zhou , Ye Deng , Yang Wu , Jinjun Wang

{"title":"Gradient-guided channel masking for cross-domain few-shot learning","authors":"Siqi Hui , Sanping Zhou , Ye Deng , Yang Wu , Jinjun Wang","doi":"10.1016/j.knosys.2024.112548","DOIUrl":"10.1016/j.knosys.2024.112548","url":null,"abstract":"<div><div>Cross-Domain Few-Shot Learning (CD-FSL) addresses the Few-Shot Learning with a domain gap between source and target domains, which facilitates the transfer of knowledge from a source domain to a target domain with limited labeled samples. Current approaches often incorporate an auxiliary target dataset containing a few labeled samples to enhance model generalization on specific target domains. However, we observe that many models retain a substantial number of channels that learn source-specific knowledge and extract features that perform adequately on the source domain but generalize poorly to the target domain. This often results in compromised performance due to the influence of source-specific knowledge. To address this challenge, we introduce a novel framework, Gradient-Guided Channel Masking (GGCM), designed for CD-FSL to mitigate model channels from acquiring too much source-specific knowledge. GGCM quantifies each channel’s contribution to solving target tasks using gradients of target loss and identifies those with smaller gradients as source-specific. These channels are then masked during the forward propagation of source features to mitigate the learning of source-specific knowledge. Conversely, GGCM mutes non-source-specific channels during the forward propagation of target features, forcing the model to depend on the source-specific channels and thereby enhancing their generalizability. Moreover, we propose a consistency loss that aligns the predictions made by source-specific channels with those made by the entire model. This approach further enhances the generalizability of these channels by enabling them to learn from the generalizable knowledge contained in other non-source-specific channels. Validated across multiple CD-FSL benchmark datasets, our framework demonstrates state-of-the-art performance and effectively suppresses the learning of source-specific knowledge.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hypothetical defenses-based training framework for generating transferable adversarial examples 基于假设防御的训练框架，用于生成可转移的对抗性实例

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-08 DOI: 10.1016/j.knosys.2024.112602

Lingguang Hao , Kuangrong Hao , Yaochu Jin , Hongzhi Zhao

{"title":"A hypothetical defenses-based training framework for generating transferable adversarial examples","authors":"Lingguang Hao , Kuangrong Hao , Yaochu Jin , Hongzhi Zhao","doi":"10.1016/j.knosys.2024.112602","DOIUrl":"10.1016/j.knosys.2024.112602","url":null,"abstract":"<div><div>Transfer-based attacks utilize the proxy model to craft adversarial examples against the target model and make significant advancements in the realm of black-box attacks. Recent research suggests that these attacks can be enhanced by incorporating adversarial defenses into the training process of adversarial examples. Specifically, adversarial defenses supervise the training process, forcing the attacker to overcome greater challenges and produce more robust adversarial examples with enhanced transferability. However, current methods mainly rely on limited input transformation defenses, which apply only linear affine changes. These defenses are insufficient for effectively removing harmful content from adversarial examples, resulting in restricted improvements in their transferability. To address this issue, we propose a novel training framework named Transfer-based Attacks through Hypothesis Defense (TA-HD). This framework enhances the generalization of adversarial examples by integrating a hypothesis defense mechanism into the proxy model. Specifically, we propose an input denoising network as the hypothesis defense to effectively remove harmful noise from adversarial examples. Furthermore, we introduce an adversarial training strategy and design specific adversarial loss functions to optimize the input denoising network’s parameters. The visualization of the training process demonstrates the effective denoising capability of the hypothesized defense mechanism and the stability of the training process. Extensive experiments show that the proposed training framework significantly improves the success rate of transfer-based attacks by up to 19.9%. The code is available at <span><span>https://github.com/haolingguang/TA-HD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BYDSEX: Binary Young's double-slit experiment optimizer with adaptive crossover for feature selection: Investigating performance issues of network intrusion detection BYDSEX：二元杨氏双缝实验优化器，用于特征选择的自适应交叉：调查网络入侵检测的性能问题

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-06 DOI: 10.1016/j.knosys.2024.112589

Doaa El-Shahat , Mohamed Abdel-Basset , Nourhan Talal , Abduallah Gamal , Mohamed Abouhawwash

{"title":"BYDSEX: Binary Young's double-slit experiment optimizer with adaptive crossover for feature selection: Investigating performance issues of network intrusion detection","authors":"Doaa El-Shahat , Mohamed Abdel-Basset , Nourhan Talal , Abduallah Gamal , Mohamed Abouhawwash","doi":"10.1016/j.knosys.2024.112589","DOIUrl":"10.1016/j.knosys.2024.112589","url":null,"abstract":"<div><div>Contemporary advancements in technology provide vast quantities of data with large dimensions, leading to high computing burdens. These big data quantities suffer from irrelevant, redundant, and noisy features. Hence, Feature Selection (FS) has become a crucial task to identify the optimal subsets of features. This research proposes a Binary version of Young's Double-Slit Experiment optimizer (BYDSE) with crossover operation (BYDSEX) for tackling FS issues. Furthermore, the proposed algorithm employs the <em>V-shaped</em> transfer function to convert continuous solutions generated by the standard YDSE into binary ones. To assess the new solutions, we employ a well-known wrapper approach, K-Nearest Neighbors (KNN), which uses the Euclidean distance metric. We integrate an adaptive crossover with a bitwise AND operation into the suggested algorithm to enhance its exploration and population diversity. Moreover, the bitwise AND operation transfers the most informative and beneficial features to the new solutions. We compared BYDSEX with nine of the most recent and powerful algorithms using 31 large-scale datasets to demonstrate its efficacy. Moreover, our BYDSEX optimizer is utilized to detect the DDoS attacks faced by most IoT devices and contemporary technologies, using six datasets extracted from CIC-DDoS2019 and NSL-KDD. Various performance metrics are utilized to assess the algorithms, such as the accuracy, the selected feature size the fitness values, the fitness values, and the time. Two statistical tests are carried out, like paired-samples T and the Wilcoxon signed-rank. BYDSEX achieved superior results compared to its competitors for most of the datasets. Furthermore, BYDSEX obtains average accuracy values of 99.78%, 99.89%, 99.69% and 99.48% for LDAP and MSSQL, NETBIOS and NSL-KDD, respectively.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ACP-Net: Asymmetric Center Positioning Network for Real-Time Text Detection ACP-Net：用于实时文本检测的非对称中心定位网络

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-06 DOI: 10.1016/j.knosys.2024.112603

Boyuan Zhu , Fagui Liu , Xi Chen , Quan Tang , C.L. Philip Chen

{"title":"ACP-Net: Asymmetric Center Positioning Network for Real-Time Text Detection","authors":"Boyuan Zhu , Fagui Liu , Xi Chen , Quan Tang , C.L. Philip Chen","doi":"10.1016/j.knosys.2024.112603","DOIUrl":"10.1016/j.knosys.2024.112603","url":null,"abstract":"<div><div>Scene text detection is crucial across numerous application fields. However, despite the emphasis on real-time performance in scene text detection, most existing detection models utilize the Feature Pyramid Network (FPN) for feature extraction, often disregarding its inherent limitations. Integrating high-resolution multi-channel features into FPN requires substantial computational resources. While FPN treats local and global features equally and is stable in various applications, its suitability for text-specific features is questionable. To this end, we propose the Asymmetric Center Positioning Network (ACP-Net) to replace FPN, achieving accuracy and real-time text detection in complex scenarios. ACP-Net features an asymmetric feature structure with independent branches for global and local information, along with an adaptive weighted fusion module to capture long-range dependencies effectively. In addition, a text center positioning module enhances text feature understanding by learning feature centers. Comprehensive evaluations across various terminals confirmed ACP-Net’s superior accuracy and speed.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0