Knowledge-Based Systems最新文献

{"title":"Semi-supervised pairwise transfer learning based on multi-source domain adaptation: A case study on EEG-based emotion recognition","authors":"Chao Ren ,&nbsp;Jinbo Chen ,&nbsp;Rui Li ,&nbsp;Weihao Zheng ,&nbsp;Yijiang Chen ,&nbsp;Yikun Yang ,&nbsp;Xiaowei Zhang ,&nbsp;Bin Hu","doi":"10.1016/j.knosys.2024.112669","DOIUrl":"10.1016/j.knosys.2024.112669","url":null,"abstract":"<div><div>Negative transfer mitigation in transfer learning and universal-model establishment are crucial in electroencephalography (EEG)-based emotion recognition research. This study proposed a multi-source domain adaptation pairwise transfer learning method (named PLMSDANet) for EEG-based emotion recognition. PLMSDANet reduced the impact of negative transfer using a semi-supervised strategy that introduced a limited set of target-labeled data and selected the most compatible source domains for further training. In addition, a two-stage feature extractor was employed. Initially, we used a general feature extractor to capture the common spatial–spectral features of all the domains. Subsequently, we created independent branches for each pair of source and target domains to learn specific features from each source domain, incorporating discrepancy loss to harmonize the classification results of the different source domains. Furthermore, pairwise learning was used to solve the problem of the intra-domain distribution of sample classes. Finally, a cross-subject strategy was used to validate the public datasets SEED and SEED-IV extensively, achieving average emotion recognition accuracies of 90.09% and 73.08%, respectively. In summary, PLMSDANet combines multi-source domain transfer learning with semi-supervised paired learning methods, effectively transferring knowledge from multiple source domains to the target domain while enhancing the distinguishability between classes. The experimental results show that the PLMSDANet method effectively mitigates the negative-transfer issue and demonstrates excellent recognition performance, surpassing that of state-of-the-art methods.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112669"},"PeriodicalIF":7.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578534","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":"Enhancing collaboration in multi-agent reinforcement learning with correlated trajectories","authors":"Siying Wang ,&nbsp;Hongfei Du ,&nbsp;Yang Zhou ,&nbsp;Zhitong Zhao ,&nbsp;Ruoning Zhang ,&nbsp;Wenyu Chen","doi":"10.1016/j.knosys.2024.112665","DOIUrl":"10.1016/j.knosys.2024.112665","url":null,"abstract":"<div><div>Collaborative behaviors in human social activities can be modeled with multi-agent reinforcement learning and used to train the collaborative policies of agents to achieve efficient cooperation. In general, agents with similar behaviors have a certain behavioral common cognition and are more likely to understand the intentions of both parties then to form cooperative policies. Traditional approaches focus on the collaborative allocation process between agents, ignoring the effects of similar behaviors and common cognition characteristics in collaborative interactions. In order to better establish collaborative relationships between agents, we propose a novel multi-agent reinforcement learning collaborative algorithm based on the similarity of agents’ behavioral features. In this model, the interactions of agents are established as a graph neural network. Specifically, the Pearson correlation coefficient is proposed to compute the similarity of the history trajectories of the agents as a means of determining their behavioral common cognition, which is used to establish the weights of the edges in the modeled graph neural network. In addition, we design a transformer-encoder structured state information complementation module to enhance the decision representation of the agents. The experimental results on Predator–Prey and StarCraft II show that the proposed method can effectively enhance the collaborative behaviors between agents and improve the training efficiency of collaborative models.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112665"},"PeriodicalIF":7.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553655","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":"CSP-Net: Common spatial pattern empowered neural networks for EEG-based motor imagery classification","authors":"Xue Jiang,&nbsp;Lubin Meng,&nbsp;Xinru Chen,&nbsp;Yifan Xu,&nbsp;Dongrui Wu","doi":"10.1016/j.knosys.2024.112668","DOIUrl":"10.1016/j.knosys.2024.112668","url":null,"abstract":"<div><div>Electroencephalogram-based motor imagery (MI) classification is an important paradigm of non-invasive brain–computer interfaces. Common spatial pattern (CSP), which exploits different energy distributions on the scalp while performing different MI tasks, is very popular in MI classification. Convolutional neural networks (CNNs) have also achieved great success, due to their powerful learning capabilities. This paper proposes two CSP-empowered neural networks (CSP-Nets), which integrate knowledge-driven CSP filters with data-driven CNNs to enhance the performance in MI classification. CSP-Net-1 directly adds a CSP layer before a CNN to improve the input discriminability. CSP-Net-2 replaces a convolutional layer in CNN with a CSP layer. The CSP layer parameters in both CSP-Nets are initialized with CSP filters designed from the training data. During training, they can either be kept fixed or optimized using gradient descent. Experiments on four public MI datasets demonstrated that the two CSP-Nets consistently improved over their CNN backbones, in both within-subject and cross-subject classifications. They are particularly useful when the number of training samples is very small. Our work demonstrates the advantage of integrating knowledge-driven traditional machine learning with data-driven deep learning in EEG-based brain–computer interfaces.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112668"},"PeriodicalIF":7.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573326","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":"Fusing YOLOv5s-MediaPipe-HRV to classify engagement in E-learning: From the perspective of external observations and internal factors","authors":"Jie Wang ,&nbsp;Shuiping Yuan ,&nbsp;Tuantuan Lu ,&nbsp;Hao Zhao ,&nbsp;Yongxiang Zhao","doi":"10.1016/j.knosys.2024.112670","DOIUrl":"10.1016/j.knosys.2024.112670","url":null,"abstract":"<div><div>The rapid advancements in computer vision technology present significant potential for the automatic recognition of learner engagement in E-learning. We conducted a two-stage experiment to assess learner engagement based on behavioural (external observations) and physiological (internal factors) cues. Using computer vision technology and wearable sensors, we extracted three feature sets: action, head posture and heart rate variability (HRV). Subsequently, we integrated our constructed YOLOv5s–MediaPipe behaviour detection model with a physiological detection model based on HRV to comprehensively evaluate learners’ behavioural, affective and cognitive engagement. Additionally, we developed a method and criteria for assessing distraction based on behaviour, ultimately creating a comprehensive, efficient, low-cost and easy-to-use system for the automatic recognition of learner engagement. Experimental results showed that our improved YOLOv5s model achieved a mean average precision of 92.2 %, while halving both the number of parameters and model size. Unlike other deep learning-based methods, using MediaPipe–OpenCV for head posture analysis offers advantages in real-time performance, making it lightweight and easy to deploy. Our proposed long short-term memory classifier, based on sensitive HRV metrics and their normalisation, demonstrated satisfactory performance on the test set, with an accuracy = 80 %, precision = 81 %, recall = 80 % and an F1 score = 80 %.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112670"},"PeriodicalIF":7.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587439","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":"DeSGOA: double exponential smoothing gazelle optimization algorithm-based deep learning model for blind source separation","authors":"N Muhsina ,&nbsp;Beegum J Dhoulath","doi":"10.1016/j.knosys.2024.112626","DOIUrl":"10.1016/j.knosys.2024.112626","url":null,"abstract":"<div><div>Blind Source Separation (BSS) pertains to a scenario, wherein the sources, as the method used for mixing are not known; only the mixed signals are accessible for subsequent separation. In several applications, it is preferable to retrieve all sources from the mixed signal or, at least isolate a specific source. The research proposes a novel approach, named Double Exponential Smoothing Gazelle Optimization Algorithm-based Generative Adversarial Network (DeSGOA-based GAN), and designed for BSS. The proposed algorithm, DeSGOA combines the power of Double Exponential Smoothing (DES) with the efficiency of the Gazelle Optimization Algorithm (GOA) to achieve superior results in source separation tasks. The research aims to enhance the accuracy and performance of BSS processes using the presented approach. At first, the mixed input signals attained from the dataset are fed to pre-processing phase. This phase aspires to eradicate noise present in the signal via the application of PCA model. The final objective is to capture a important amount of data information in the reduced dataset. Following this, the BSS process is carried out by utilizing the GAN, which is trained through the innovative DeSGOA algorithm. Experimental outcomes illustrate the efficacy of DeSGOA-based GAN method in achieving high-quality source separation, underscoring its potential as a valuable tool in audio signal processing and related applications. Finally, the experimental evaluation illustrated that the presented strategy gained SDR of 35.05, SIR of 11.94, SAR of 8.247, and ISR of 13.02.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112626"},"PeriodicalIF":7.2,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529406","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":"CGG: Category-aware global graph contrastive learning for session-based recommendation","authors":"Mingxin Gan,&nbsp;Xiongtao Zhang,&nbsp;Yuxin Liang","doi":"10.1016/j.knosys.2024.112661","DOIUrl":"10.1016/j.knosys.2024.112661","url":null,"abstract":"<div><div>With the auxiliary role of category information in capturing user interests, employing category information to improve session-based recommendation (SBR) is getting an energetic research point. Recent studies organized the category-aware session as the graph structure and utilized the graph neural network to explore the session interest for SBR. However, existing studies only focused on the category information in the current session and failed to overcome inherent sparsity of session data, which resulted in suboptimal SBR performance. To overcome these deficiencies, we propose a <strong>C</strong>ategory-aware <strong>G</strong>lobal <strong>G</strong>raph contrastive learning method, namely CGG, for SBR. To be specific, we firstly construct the category-aware global graph based on global item-item transitions, item-category associations and global category-category transitions, which utilizes more sufficient category information across sessions to learn embeddings of categories and items. Furthermore, we design the hierarchical dual-pattern contrastive learning mechanism to model the information interaction of graphical and sequential patterns of a category-aware session, which overcomes the negative influence of sparse session data by injecting self-supervised signals. Extensive experiments on multiple real-world datasets verify that CGG outperforms seven mainstream SBR methods on different measurements.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112661"},"PeriodicalIF":7.2,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573328","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":"Dual semi-supervised hypergraph regular multi-view NMF with anchor graph embedding","authors":"Jianping Mei ,&nbsp;Xiangli Li ,&nbsp;Yuanjian Mo","doi":"10.1016/j.knosys.2024.112662","DOIUrl":"10.1016/j.knosys.2024.112662","url":null,"abstract":"<div><div>Graph regularized nonnegative matrix factorization (GNMF) has been widely used in multi-view clustering tasks due to its good clustering properties. However, it uses a simple graph to describe the complex data relationship of multiple views and tries to obtain a consistent low-dimensional representation, which undoubtedly brings challenges to its clustering performance. In addition, clustering a large amount of high-dimensional data from multiple views undoubtedly faces a huge computational burden. In order to effectively improve the performance and efficiency of GNMF based multi-view clustering algorithm, this paper proposes a dual semi-supervised hypergraph regular multi-view clustering method with anchor graph embedding (DSSHMNMFAE). Specifically, DSSHMNMFAE develops a new anchor selection method to generate anchors and the anchor bipartite graph is constructed to embed the matrix factorization process. DSSHMNMFAE constructs a hypergraph to effectively learn the high-order relationship between data from multiple views. In order to perform semi-supervised learning more efficiently, DSSHMNMFAE integrates pairwise constraint information and label constraint information into the clustering process as dual label information. In addition, DSSHMNMFAE considers the learning of both consistency information and complementarity information, and adopts adaptive measures to distinguish the contributions of different views. We use the alternating iterative algorithm to optimize the objective function of DSSHMNMFAE. The experimental results on eight real datasets show that the performance of DSSHMNMFAE is comparable to other algorithms.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112662"},"PeriodicalIF":7.2,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553726","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":"Rethinking self-supervised learning for time series forecasting: A temporal perspective","authors":"Shubao Zhao ,&nbsp;Xinxing Zhou ,&nbsp;Ming Jin ,&nbsp;Zhaoxiang Hou ,&nbsp;Chengyi Yang ,&nbsp;Zengxiang Li ,&nbsp;Qingsong Wen ,&nbsp;Yi Wang ,&nbsp;Yanlong Wen ,&nbsp;Xiaojie Yuan","doi":"10.1016/j.knosys.2024.112652","DOIUrl":"10.1016/j.knosys.2024.112652","url":null,"abstract":"<div><div>Self-supervised learning has garnered significant attention for its ability to learn meaningful representations. Recent advancements have introduced self-supervised methods for time series forecasting. However, these efforts have faced limitations due to two primary drawbacks. Firstly, these approaches often borrow techniques from vision and language domains without adequately addressing the unique temporal dependencies inherent in time series data. Secondly, time series often show that the distribution shifts over time, which makes accurate forecasting challenging. In response to these issues, we propose TempSSL, a self-supervised learning framework designed for time series forecasting. TempSSL divides the time series data into context (history data) and target (future data), employing two pre-training strategies: (1) Temporal Masked Modeling (TMM) designed to capture temporal dependencies by reconstructing future time series based on historical context; (2) Temporal Contrastive Learning (TCL) employs context and target as positive samples to enhance discriminative representations and mitigate distribution shifts within the time series. TempSSL’s innovation lies in two key aspects. Firstly, it underscores the importance of temporal dependencies for time series forecasting by designing specific pre-training tasks. Secondly, it effectively integrates contrastive learning and masked modeling, leveraging their respective strengths to develop time series representation with strong instance discriminability and local perceptibility. Extensive experiments across seven widely used benchmark datasets demonstrate that TempSSL consistently outperforms existing self-supervised and end-to-end forecasting methods, achieving improvements ranging from 1.92% <span><math><mo>∼</mo></math></span> 78.12%. Additionally, TempSSL’s practical effectiveness is further demonstrated through successful application in natural gas demand forecasting.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112652"},"PeriodicalIF":7.2,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573468","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":"Clustering dynamic networks by discriminating roles of vertices and capturing temporality with subsequent feature projection","authors":"Yaxiong Ma ,&nbsp;Yue Gao ,&nbsp;Zengfa Dou ,&nbsp;Guohua Huang ,&nbsp;Xiaoke Ma","doi":"10.1016/j.knosys.2024.112660","DOIUrl":"10.1016/j.knosys.2024.112660","url":null,"abstract":"<div><div>Clustering dynamic networks has gained popularity due to the need to analyze complex systems that evolve over time, which cannot be fully characterized by traditional static models. It is highly non-trivial in comparison to clustering static network since it requires simultaneously to balance clustering accuracy and clustering drift, where clustering accuracy measures how clustering reflects structure of graph at current time, and clustering drift quantifies how clustering smoothes historical snapshot(s). In this study, we propose an algorithm <u><strong>c</strong></u>lustering <u><strong>d</strong></u>ynamic <u><strong>n</strong></u>etwork by <u><strong>d</strong></u>iscriminating <u><strong>r</strong></u>oles of vertices and <u><strong>c</strong></u>apturing <u><strong>t</strong></u>emporality with subsequent feature projection (<strong>CDN-DRCT</strong>). Specifically, clustering accuracy is achieved by factorizing high-order matrix of slice at current time, and vertices are divided into static and dynamic ones by the reconstruction errors. Finally, the proposed algorithm measures temporality of networks with a projection matrix, which connects subsequent features at the previous and current time, thereby enhancing clustering drift of clusters. In this case, temporality of dynamic networks is characterized from vertex and global level, providing a better way to balance clustering accuracy and clustering drift. Experimental results on 10 typical dynamic networks demonstrate the proposed algorithm is superior to baselines in terms of accuracy as well efficiency.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112660"},"PeriodicalIF":7.2,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553729","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":"Dual De-confounded Causal Intervention method for knowledge graph error detection","authors":"Yunxiao Yang,&nbsp;Jianting Chen,&nbsp;Xiaoying Gao,&nbsp;Yang Xiang","doi":"10.1016/j.knosys.2024.112644","DOIUrl":"10.1016/j.knosys.2024.112644","url":null,"abstract":"<div><div>Due to the Knowledge Graph (KG) construction process, erroneous triples are virtually inevitable to be introduced into real-world KGs. Since these errors hinder the expressiveness and applicability of KGs, the development of knowledge graph error detection (KGED) methods is necessary. Despite the overall effectiveness of current KGED methods, their capacity to identify challenging errors is limited. In this work, we conduct empirical studies and find that previous works introduce structural and semantic bias, impeding the identification of erroneous triples, especially in challenging cases. To address this issue, we design a causal graph for the KGED task and propose a Dual De-confounded Causal Intervention (DuDCI) method for debiasing. Firstly, DuDCI utilizes the neighborhood and textual descriptions of triples to calculate their graph and text embeddings. Next, a Causal De-confounded Module is constructed to mitigate the impact of shortcuts caused by the bias through the front-door adjustment. Furthermore, we introduce Disentanglement Constraints to disentangle the information expressed by each embedding, thereby facilitating further bias mitigation. Experimental results on three widely used KGED datasets validate the effectiveness of DuDCI and demonstrate that DuDCI outperforms current KGED methods, with an improvement of at least 2.2%, especially in more challenging noise scenarios.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112644"},"PeriodicalIF":7.2,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553730","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}