Knowledge-Based Systems最新文献

{"title":"Robust deadline-aware network function parallelization framework under demand uncertainty","authors":"Bo Meng ,&nbsp;Amin Rezaeipanah","doi":"10.1016/j.knosys.2024.112696","DOIUrl":"10.1016/j.knosys.2024.112696","url":null,"abstract":"<div><div>The orchestration of Service Function Chains (SFCs) in Mobile Edge Computing (MEC) becomes crucial for ensuring efficient service provision, especially under dynamic and uncertain demand. Meanwhile, the parallelization of Virtual Network Functions (VNFs) within an SFC can further optimize resource usage and reduce the risk of deadline violations. However, most existing works formulate the SFC orchestration problem in MEC with deterministic demands and costly runtime resource reprovisioning to handle dynamic demands. This paper introduces a Robust Deadline-aware network function Parallelization framework under Demand Uncertainty (RDPDU) designed to address the challenges posed by unpredictable fluctuations in user demand and resource availability within MEC networks. RDPDU to consider end-to-end latency for SFC assembly by modeling load-dependent processing latency and load-independent propagation latency. Also, RDPDU formulates the problem assuming uncertain demand by Quadratic Integer Programming (QIP) to be resistant to dynamic service demand fluctuations. By discovering dependencies between VNFs, the RDPDU effectively assembles multiple sub-SFCs instead of the original SFC. Finally, our framework uses Deep Reinforcement Learning (DRL) to assemble sub-SFCs with guaranteed latency and deadline. By integrating DRL into the SFC orchestration problem, the framework adapts to changing network conditions and demand patterns, improving the overall system's flexibility and robustness. Experimental evaluations show that the proposed framework can effectively deal with demand fluctuations, latency, deadline, and scalability and improve performance against recent algorithms.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112696"},"PeriodicalIF":7.2,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594096","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}

{"title":"MuGIL: A Multi-Graph Interaction Learning Network for Multi-Task Traffic Prediction","authors":"Shuai Liu ,&nbsp;Haiyang Yu ,&nbsp;Han Jiang ,&nbsp;Zhenliang Ma ,&nbsp;Zhiyong Cui ,&nbsp;Yilong Ren","doi":"10.1016/j.knosys.2024.112709","DOIUrl":"10.1016/j.knosys.2024.112709","url":null,"abstract":"<div><div>Recently, multi-task traffic prediction has received increasing attention, as it enables knowledge sharing between heterogeneous variables or regions, thereby improving prediction accuracy while satisfying the prediction requirements of multi-source data in Intelligent Transportation Systems (ITS). However, current studies present two significant challenges. First, they often tend to construct specialized models for a limited set of predictive parameters, which results in a lack of generality. Second, modeling the graph-based multi-task interaction and message passing processes remains difficult due to the heterogeneity of graph structures arising from multi-source traffic data. To address these challenges, this paper proposes a Multi-Graph Interaction Learning Network (MuGIL), characterized by three key innovations: 1) A flexible end-to-end multi-task prediction framework that is generalizable for varied variables or scenarios; 2) A multi-source graph representation module that aligns heterogeneous information through semantic graphs; 3) A novel message passing mechanism for multi-task graph neural networks, which enables effective knowledge among tasks. The model is validated using data from California by comparing it with the state-of-the-art prediction models. The results show that the MuGIL model achieves better prediction performance than these baselines. Ablation experiments further highlight the critical role of the designed multi-source graph representation module and message passing mechanism in the model's success. The MuGIL model we have proposed is now open-sourced at the following link: <span><span>https://github.com/trafficpre/MuGIL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112709"},"PeriodicalIF":7.2,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705008","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}

{"title":"Multi-view representation learning with dual-label collaborative guidance","authors":"Bin Chen ,&nbsp;Xiaojin Ren ,&nbsp;Shunshun Bai ,&nbsp;Ziyuan Chen ,&nbsp;Qinghai Zheng ,&nbsp;Jihua Zhu","doi":"10.1016/j.knosys.2024.112680","DOIUrl":"10.1016/j.knosys.2024.112680","url":null,"abstract":"<div><div>Multi-view Representation Learning (MRL) has recently attracted widespread attention because it can integrate information from diverse data sources to achieve better performance. However, existing MRL methods still have two issues: (1) They typically perform various consistency objectives within the feature space, which might discard complementary information contained in each view. (2) Some methods only focus on handling inter-view relationships while ignoring inter-sample relationships that are also valuable for downstream tasks. To address these issues, we propose a novel Multi-view representation learning method with Dual-label Collaborative Guidance (MDCG). Specifically, we fully excavate and utilize valuable semantic and graph information hidden in multi-view data to collaboratively guide the learning process of MRL. By learning consistent semantic labels from distinct views, our method enhances intrinsic connections across views while preserving view-specific information, which contributes to learning the consistent and complementary unified representation. Moreover, we integrate similarity matrices of multiple views to construct graph labels that indicate inter-sample relationships. With the idea of self-supervised contrastive learning, graph structure information implied in graph labels is effectively captured by the unified representation, thus enhancing its discriminability. Extensive experiments on diverse real-world datasets demonstrate the effectiveness and superiority of MDCG compared with nine state-of-the-art methods. Our code will be available at <span><span>https://github.com/Bin1Chen/MDCG</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112680"},"PeriodicalIF":7.2,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594095","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}

{"title":"PMCN: Parallax-motion collaboration network for stereo video dehazing","authors":"Chang Wu ,&nbsp;Gang He ,&nbsp;Wanlin Zhao ,&nbsp;Xinquan Lai ,&nbsp;Yunsong Li","doi":"10.1016/j.knosys.2024.112681","DOIUrl":"10.1016/j.knosys.2024.112681","url":null,"abstract":"<div><div>Despite progress in learning-based stereo dehazing, few studies have focused on stereo video dehazing (SVD). Existing methods may fall short in the SVD task by not fully leveraging multi-domain information. To address this gap, we propose a parallax-motion collaboration network (PMCN) that integrates parallax and motion information for efficient stereo video fog removal. We delicately design a parallax-motion collaboration block (PMCB) as the critical component of PMCN. Firstly, to capture binocular parallax correspondences more efficiently, we introduce a window-based parallax attention mechanism (W-PAM) in the parallax interaction module (PIM) of PMCB. By horizontally splitting the whole frame into multiple windows and extracting parallax relationships within each window, memory usage and runtime can be reduced. Meanwhile, we further conduct horizontal feature modulation to handle cross-window disparity variations. Secondly, a motion alignment module (MAM) based on deformable convolution explores the temporal correlation in the feature space for an independent view. Finally, we propose a fog-adaptive refinement module (FARM) to refine the features after interaction and alignment. FARM incorporates fog prior information and guides the network in dynamically generating processing kernels for dehazing to adapt to different fog scenarios. Quantitative and qualitative results demonstrate that the proposed PMCN outperforms state-of-the-art methods on both synthetic and real-world datasets. In addition, our PMCN also benefits the accuracy improvement for high-level vision tasks in fog scenes, e.g., object detection and stereo matching.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112681"},"PeriodicalIF":7.2,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594094","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}

{"title":"Path relinking strategies for the bi-objective double floor corridor allocation problem","authors":"Nicolás R. Uribe,&nbsp;Alberto Herrán,&nbsp;J. Manuel Colmenar","doi":"10.1016/j.knosys.2024.112666","DOIUrl":"10.1016/j.knosys.2024.112666","url":null,"abstract":"<div><div>The bi-objective Double Floor Corridor Allocation Problem is an operational research problem with the goal of finding the best arrangement of facilities in a layout with two corridors located in two floors, in order to minimize the material handling costs and the corridor length. In this paper, we present a novel approach based on a combination of Path Relinking strategies. To this aim, we propose two greedy algorithms to produce an initial set of non-dominated solutions. In a first stage, we apply an Interior Path Relinking with the aim of improving this set and, in the second stage, apply an Exterior Path Relinking to reach solutions that are unreachable in the first stage. Our extensive experimental analysis shows that our method, after automatic parameter optimization, completely dominates the previous benchmarks, spending shorter computation times. In addition, we provide detailed results for the new instances, including standard metrics for multi-objective problems.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112666"},"PeriodicalIF":7.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587440","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}

{"title":"MFGCN: Multi-faceted spatial and temporal specific graph convolutional network for traffic-flow forecasting","authors":"Jingwen Tian ,&nbsp;Liangzhe Han ,&nbsp;Mao Chen ,&nbsp;Yi Xu ,&nbsp;Zhuo Chen ,&nbsp;Tongyu Zhu ,&nbsp;Leilei Sun ,&nbsp;Weifeng Lv","doi":"10.1016/j.knosys.2024.112671","DOIUrl":"10.1016/j.knosys.2024.112671","url":null,"abstract":"<div><div>Traffic-flow forecasting is a fundamental issue in Intelligent Transportation Systems. Owing to the natural topological structure of road networks, graph convolutional networks (GCNs) have become one of the most promising components. However, existing methods usually implement graph convolution on a static adjacent matrix to capture the spatial relations between road segments, ignoring the fact that the spatial impact varies across time. Moreover, they always learn the common temporal relations for all segments and fail to capture unique patterns for each distinct node. To address these issues, this study explores time-specific spatial dependencies and node-specific temporal relations to utilize GCN for improved traffic-flow forecasting. First, graph convolution is extended to learn the temporal relations between different time slots. The trained graphs contain unique temporal patterns for each node and share patterns among different nodes. Second, a time-specific spatial graph-learning module is designed to establish dynamic spatial dependencies between traffic nodes, which can vary at different times. Finally, an adaptive pattern-sharing mechanism is proposed to adaptively learn the layer-specific patterns and sharing-across-layer patterns. The proposed model is evaluated on four public real-world traffic datasets, and the results show that it outperforms all state-of-the-art methods on the four real-world datasets.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112671"},"PeriodicalIF":7.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705193","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}

{"title":"Dynamical mode recognition of coupled flame oscillators by supervised and unsupervised learning approaches","authors":"Weiming Xu ,&nbsp;Tao Yang ,&nbsp;Peng Zhang","doi":"10.1016/j.knosys.2024.112683","DOIUrl":"10.1016/j.knosys.2024.112683","url":null,"abstract":"<div><div>Combustion instability in gas turbines and rocket engines, as one of the most challenging problems in combustion research, arises from the complex interactions among flames influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential for ensuring the safe and reliable operation of many combustion systems, where exploring and classifying the dynamical behaviors of complex flame systems is a core task. To facilitate fundamental studies, the present work concerned dynamical mode recognition of coupled flame oscillators made of flickering buoyant diffusion flames, which have gained increasing attention in recent years but are not sufficiently understood. The time series data of flame oscillators were generated through fully validated reacting flow simulations. Due to the limitations of expertise-based models, a data-driven approach was adopted. In this study, a nonlinear dimensional reduction model of variational autoencoder (VAE) was used to project the high dimensional data onto a 2-dimensional latent space. Based on phase trajectories in the latent space, both supervised and unsupervised classifiers were proposed for datasets with and without well-known labeling, respectively. For labeled datasets, we established the Wasserstein-distance-based classifier (WDC) for mode recognition; for unlabeled datasets, we developed a novel unsupervised classifier (GMM-DTW) combining dynamic time warping (DTW) and Gaussian mixture model (GMM). Through comparing with conventional approaches for dimensionality reduction and classification, the proposed supervised and unsupervised VAE-based approaches exhibit a prominent performance across seven assessment metrics for distinguishing dynamical modes, implying their potential extension to dynamical mode recognition in complex combustion problems.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"307 ","pages":"Article 112683"},"PeriodicalIF":7.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698127","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}

{"title":"Multi-view multi-behavior interest learning network and contrastive learning for multi-behavior recommendation","authors":"Jieyang Su,&nbsp;Yuzhong Chen,&nbsp;Xiuqiang Lin,&nbsp;Jiayuan Zhong,&nbsp;Chen Dong","doi":"10.1016/j.knosys.2024.112604","DOIUrl":"10.1016/j.knosys.2024.112604","url":null,"abstract":"<div><div>The recommendation system aims to recommend items to users by capturing their personalized interests. Traditional recommendation systems typically focus on modeling target behaviors between users and items. However, in practical application scenarios, various types of behaviors (e.g., click, favorite, purchase, etc.) occur between users and items. Despite recent efforts in modeling various behavior types, multi-behavior recommendation still faces two significant challenges. The first challenge is how to comprehensively capture the complex relationships between various types of behaviors, including their interest differences and interest commonalities. The second challenge is how to solve the sparsity of target behaviors while ensuring the authenticity of information from various types of behaviors. To address these issues, a multi-behavior recommendation framework based on Multi-View Multi-Behavior Interest Learning Network and Contrastive Learning (MMNCL) is proposed. This framework includes a multi-view multi-behavior interest learning module that consists of two submodules: the behavior difference aware submodule, which captures intra-behavior interests for each behavior type and the correlations between various types of behaviors, and the behavior commonality aware submodule, which captures the information of interest commonalities between various types of behaviors. Additionally, a multi-view contrastive learning module is proposed to conduct node self-discrimination, ensuring the authenticity of information integration among various types of behaviors, and facilitating an effective fusion of interest differences and interest commonalities. Experimental results on three real-world benchmark datasets demonstrate the effectiveness of MMNCL and its advantages over other state-of-the-art recommendation models. Our code is available at <span><span>https://github.com/sujieyang/MMNCL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112604"},"PeriodicalIF":7.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553654","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}

{"title":"FS-PTL: A unified few-shot partial transfer learning framework for partial cross-domain fault diagnosis under limited data scenarios","authors":"Liu Cheng ,&nbsp;Haochen Qi ,&nbsp;Rongcai Ma ,&nbsp;Xiangwei Kong ,&nbsp;Yongchao Zhang ,&nbsp;Yunpeng Zhu","doi":"10.1016/j.knosys.2024.112658","DOIUrl":"10.1016/j.knosys.2024.112658","url":null,"abstract":"<div><div>Traditional supervised learning-based fault-diagnosis models often encounter performance degradation when data distribution shifts occur. Although unsupervised transfer learning can address such issues, most existing methods face challenges arising from partial cross-domain diagnostic scenarios with limited training data. Therefore, this study introduces a unified few-shot partial-transfer learning framework, specifically designed to address the limitations of data scarcity and partial cross-domain diagnosis applicability. Our framework innovatively takes ridge regression-based feature reconstruction as a nexus to integrate episodic learning with an episodic pretext task and weighted feature alignment, thereby enhancing model adaptability across varying working conditions with minimal data. Specifically, the episodic pretext task enables the learned features with generalization abilities in a self-supervised manner to mitigate meta-overfitting. Weighted feature alignment is performed at the reconstructed feature level, allowing partial transfer with a significantly increased number of features, while further reducing overfitting. Experiments conducted on two distinct datasets revealed that the proposed method outperforms existing state-of-the-art approaches, demonstrating superior transfer performance and robustness under the conditions of limited fault samples.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112658"},"PeriodicalIF":7.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573334","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}

{"title":"Intelligent fault diagnosis for tribo-mechanical systems by machine learning: Multi-feature extraction and ensemble voting methods","authors":"V. Shandhoosh ,&nbsp;Naveen Venkatesh S ,&nbsp;Ganjikunta Chakrapani ,&nbsp;V. Sugumaran ,&nbsp;Sangharatna M. Ramteke ,&nbsp;Max Marian","doi":"10.1016/j.knosys.2024.112694","DOIUrl":"10.1016/j.knosys.2024.112694","url":null,"abstract":"<div><div>Timely fault detection is crucial for preventing issues like worn clutch plates and excessive friction material degradation, enhancing fuel efficiency, and prolonging clutch lifespan. This study focuses on early fault diagnosis in dry friction clutch systems using machine learning (ML) techniques. Vibration data is analyzed under different load and fault conditions, extracting statistical, histogram, and auto-regressive moving average (ARMA) features. Feature selection employs the J48 decision tree algorithm, evaluated with eight ML classifiers: support vector machines (SVM), k-nearest neighbor (kNN), linear model tree (LMT), random forest (RF), multilayer perceptron (MLP), logistic regression (LR), J48, and Naive Bayes. The evaluation revealed that individual classifiers achieved the highest testing accuracies with statistical feature selection as 83% for both MLP and LR at no load, 90% for MLP at 5 kg, and 93% for KNN at 10 kg. For histogram feature selection, KNN and MLP both reached 85% at no load, MLP achieved 91% at 5 kg, and RF attained 97% at 10 kg. With ARMA feature selection, KNN reached 93% at no load, LR achieved 94% at 5 kg, and RF reached 86% at 10 kg. The voting strategy notably improved these results, with the RF-KNN-J48 ensemble reaching 98% for histogram features at 10 kg, the KNN-LMT-RF ensemble achieving 94% for ARMA features at no load, and the SVM-MLP-LMT ensemble attaining 95% for ARMA features at 5 kg. Hence, a combination of three classifiers using the majority voting rule consistently outperforms standalone classifiers, striking a balance between diversity and complexity, facilitating robust decision-making. In practical applications, selecting the optimal combination of feature selection method and classifier is vital for accurate fault classification. This study provides valuable guidance for engineers and practitioners implementing robust load classification systems in industrial settings.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112694"},"PeriodicalIF":7.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}