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A deep spatiotemporal interaction network for multimodal sentimental analysis and emotion recognition 用于多模态情感分析和情感识别的深度时空交互网络

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-09-26 DOI: 10.1016/j.ins.2024.121515

{"title":"A deep spatiotemporal interaction network for multimodal sentimental analysis and emotion recognition","authors":"","doi":"10.1016/j.ins.2024.121515","DOIUrl":"10.1016/j.ins.2024.121515","url":null,"abstract":"<div><div>One of the challenges of sentiment analysis and emotion recognition is how to effectively fuse the multimodal inputs. The transformer-based models have achieved great success in applications of multimodal sentiment analysis and emotion recognition recently. However, the transformer-based model often neglects the coherence of human emotion due to its parallel structure. Additionally, a low-rank bottleneck created by multi- attention-head causes an inadequate fitting ability of models. To tackle these issues, a Deep Spatiotemporal Interaction Network (DSIN) is proposed in this study. It consists of two main components, i.e., a cross-modal transformer with a cross-talking attention module and a hierarchically temporal fusion module, where the cross-modal transformer is used to model the spatial interactions between different modalities and the hierarchically temporal fusion network is utilized to model the temporal coherence of emotion. Therefore, the DSIN can model the spatiotemporal interactions of multimodal inputs by incorporating the time-dependency into the parallel structure of transformer and decrease the redundancy of embedded features by implanting their spatiotemporal interactions into a hybrid memory network in a hierarchical manner. The experimental results on two benchmark datasets indicate that DSIN achieves superior performance compared with the state-of-the-art models, and some useful insights are derived from the results.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422808","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

Predefined time fuzzy adaptive control for stochastic nonlinear systems with limited time interval output constraints 具有有限时间间隔输出约束的随机非线性系统的预定义时间模糊自适应控制

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-09-26 DOI: 10.1016/j.ins.2024.121506

{"title":"Predefined time fuzzy adaptive control for stochastic nonlinear systems with limited time interval output constraints","authors":"","doi":"10.1016/j.ins.2024.121506","DOIUrl":"10.1016/j.ins.2024.121506","url":null,"abstract":"<div><div>This article investigates the problem of fuzzy adaptive predefined time tracking control for stochastic nonlinear systems with limited time interval output constraints. Firstly, the considered output constraints occur within a finite time interval after the system starts running. By constructing shift functions and barrier Lyapunov functions (BLFs), the constrained system is converted into an unconstrained system, which solves the problem of function discontinuity resulting from output constraints within limited time intervals. Then, with the help of adaptive backstepping technique and predefined time control method, which reduces the parameters to be designed and removes the limitation of relatively large initial control input in existing approaches. The designed control technique ensures the boundedness of all variables of the stochastic system, the tracking error converges within a predetermined time, and the outputs do not exceed their constraint boundaries in a finite time interval. And this algorithm is applicable to both infinite time constraints and unconstrained outputs cases. Finally, the validity of this approach is demonstrated through simulation examples.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423888","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

Hyperspectral image classification using feature fusion fuzzy graph broad network 利用特征融合模糊图广网络进行高光谱图像分类

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-09-26 DOI: 10.1016/j.ins.2024.121504

{"title":"Hyperspectral image classification using feature fusion fuzzy graph broad network","authors":"","doi":"10.1016/j.ins.2024.121504","DOIUrl":"10.1016/j.ins.2024.121504","url":null,"abstract":"<div><div>In recent years, graph convolutional networks (GCNs) have shown strong performance in hyperspectral image (HSI) classification. However, traditional GCN methods often use superpixel-based nodes to reduce computational complexity, which fails to capture pixel-level spectral-spatial features. Additionally, these methods typically focus on matching predicted labels with ground truth, neglecting the relationships between inter-class and intra-class distances, leading to less discriminative features. To address these issues, we propose a feature fusion fuzzy graph broad network (F<sup>3</sup>GBN) for HSI classification. Our method extracts pixel-level attribute contour features using attribute filters and fuses them with superpixel features through canonical correlation analysis. We employ a broad learning system (BLS) as the classifier, which fully utilizes spectral-spatial information via nonlinear transformations. Furthermore, we construct intra-class and inter-class graphs based on fuzzy set and manifold learning theories to ensure better clustering of samples within the same class and separation between different classes. A novel loss function is introduced in BLS to minimize intra-class distances and maximize inter-class distances, enhancing feature discriminability. The proposed F<sup>3</sup>GBN model achieved impressive overall accuracy on public datasets: 96.73% on Indian Pines, 98.29% on Pavia University, 98.69% on Salinas, and 99.43% on Kennedy Space Center, outperforming several classical and state-of-the-art methods, thereby demonstrating its effectiveness and feasibility.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323407","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

MRRFGNN: Multi-relation reconstruction and fusion graph neural network for stock crash prediction MRRFGNN：用于股灾预测的多相关重构与融合图神经网络

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-09-24 DOI: 10.1016/j.ins.2024.121507

{"title":"MRRFGNN: Multi-relation reconstruction and fusion graph neural network for stock crash prediction","authors":"","doi":"10.1016/j.ins.2024.121507","DOIUrl":"10.1016/j.ins.2024.121507","url":null,"abstract":"<div><div>Stock crash risk often propagates through various interconnected relationships between firms, amplifying its impact across financial markets. Few studies predicted the crash risk of one firm in terms of its relevant firms. A common strategy is to adopt graph neural networks (GNNs) with some predefined firm relations. However, many relations remain undetected or evolve over time. Restricting to several predefined relations inevitably makes noise and thus misleads stock crash predictions. In addition, these relationships are not independent during the process of propagating information and interacting with each other. This study proposes the multi-relation reconstruction and fusion graph neural network (MRRFGNN) to predict stock crash risk by capturing complex relations among listed companies. First, the model employs self-supervised learning and contrastive learning to reconstruct and infer implicit relationships between companies. Second, the model incorporates a relation self-attention mechanism to integrate various types of relationships, enabling a more nuanced understanding of the multiple spillover effects. Empirical evidence from a series of experiments demonstrates the superiority of the proposed method, which achieves the best performance with improvements of at least 2.14% in area under the curve (AUC) and 2.64% in Matthews correlation coefficient (MCC), highlighting its potential for practical application in financial markets.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358206","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

Herd behavior identification based on coevolution in human–machine collaborative multi-stage large group decision-making 人机协作多阶段大型群体决策中基于协同进化的群体行为识别

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-09-24 DOI: 10.1016/j.ins.2024.121511

{"title":"Herd behavior identification based on coevolution in human–machine collaborative multi-stage large group decision-making","authors":"","doi":"10.1016/j.ins.2024.121511","DOIUrl":"10.1016/j.ins.2024.121511","url":null,"abstract":"<div><div>As the scale of multi-stage large group decision-making (LGDM) continues to expand, the possibility of low-contribution individuals exhibiting herd behavior also increases, potentially leading to the phenomenon of “fishing in troubled waters.” This may obstruct the speed of consensus reaching while generating no valuable opinions, which is a topic worthy of exploration. Considering that humans are easily influenced by interests, the employment of machine intelligence to objectively identify herd behavior is more appropriate. In this context, a herd behavior identification method based on behavioral characteristics clustering from the perspective of human–machine collaboration is herein proposed. First, from the human side, an opinion–social network coevolution model is constructed to simulate the consensus reaching process (CRP) of the expert group. Then, the group is clustered into three subgroups in consideration of behavior that encompasses both opinion changes and trust relationship changes. Based on this, the low-contribution cluster with a herd behavior pattern can be optimized from the machine side. Through simulation experiments, it is verified that herd behavior management significantly accelerates the consensus-reaching speed under the premise of having minimal impact on the decision-making results. In general terms, this study is the first to propose the concept of herd behavior and provides a solution to manage it from a new perspective, which is suitable for application in multi-stage LGDM scenarios.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358207","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

k-plex-based community detection with graph neural networks 基于图神经网络的 k-plex 群落检测

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-09-24 DOI: 10.1016/j.ins.2024.121509

{"title":"k-plex-based community detection with graph neural networks","authors":"","doi":"10.1016/j.ins.2024.121509","DOIUrl":"10.1016/j.ins.2024.121509","url":null,"abstract":"<div><div>Community detection is an effective way to determine the structure and characteristics of a complex network. With the expansion of the network scale, traditional community detection approaches such as modularity-based optimization models face new challenges related to representing and learning the topological structure and node attributes from a large scale complex network. Moreover, in many practical applications, there is little knowledge about label information or the number of communities, which greatly limits the performance of existing supervised or semi-supervised community detection approaches. To solve these problems, in this paper, we propose a graph neural network-based unsupervised community detection approach, which first applies the <em>k</em>-plex to generate the community seeds, then uses a node sampling algorithm to reduce the network complexity, and finally constructs a graph neural network model to learn the relationships of the network nodes and assign the nodes to different communities. Extensive empirical studies on various scale networks demonstrate both the effectiveness and efficiency of the proposed approach. Our codes are available at <span><span>https://github.com/lol12854/KPGN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326809","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

Image based information hiding via minimization of entropy and randomization 通过熵最小化和随机化实现基于图像的信息隐藏

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-09-24 DOI: 10.1016/j.ins.2024.121514

{"title":"Image based information hiding via minimization of entropy and randomization","authors":"","doi":"10.1016/j.ins.2024.121514","DOIUrl":"10.1016/j.ins.2024.121514","url":null,"abstract":"<div><div>In this paper, a new approach that can effectively and securely hide information into color images with significantly improved security and hiding capacity is proposed. The proposed approach performs information hiding in three major steps. As the first step, two binary sequences are constructed from the least significant bits in the pixels of a cover image and the information that needs to be embedded, the information entropies of both sequences are minimized with a dynamic programming method. In the second step, the resulting sequences are randomly reshuffled into randomized sequences with mappings based on a set of one-dimensional chaotic systems, a single binary sequence can be obtained by a matching operation performed between the two randomized sequences. Finally, an inverse mapping is applied to the sequence obtained in the second step, and the transformed sequence is embedded into the least significant bits in the pixels of the cover image. Both analysis and experiments show that the proposed approach can achieve guaranteed performance in both security and capacity for long binary sequences. In addition, a comparison with other state-of-the-art methods for image-based information hiding suggests that the proposed approach can achieve significantly improved performance and is promising for practical applications.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326735","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

Multi-level optimizing of parameters in stochastic configuration networks based on cloud model and nutcracker optimization algorithm 基于云模型和胡桃夹子优化算法的随机配置网络参数多级优化

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-09-24 DOI: 10.1016/j.ins.2024.121495

{"title":"Multi-level optimizing of parameters in stochastic configuration networks based on cloud model and nutcracker optimization algorithm","authors":"","doi":"10.1016/j.ins.2024.121495","DOIUrl":"10.1016/j.ins.2024.121495","url":null,"abstract":"<div><div>As a state-of-the-art neural network model, stochastic configuration networks (SCNs) are widely employed in diverse fields due to their exceptional approximation capabilities. Similar to other neural network models, an excessive number of parameters can potentially compromise the generalization ability of SCNs, including hyper-parameters such as the stochastic scale factors (<em>λ</em>) and the maximum number of nodes (<span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></math></span>), as well as model parameters like input weight (<em>w</em>) and input bias (<em>b</em>). To tackle this issue, this study proposes a multi-level parameter optimization approach, termed stochastic configuration network with cloud models (CMSCNs). Firstly, the optimal parameter range is determined based on the concept of “cloud droplet” from the cloud model. Herein, mathematical expectation (<span><math><msub><mrow><mi>E</mi></mrow><mrow><mi>x</mi></mrow></msub></math></span>) is substituted by a polynomial function constructed with <em>λ</em> as the dependent variable and other parameters as independent variables. Secondly, we employ the nutcracker optimization algorithm (NOA) to optimize <em>w</em> and <em>b</em>, using residuals as evaluation indices to identify their optimal combination. Thirdly, singular value decomposition (SVD) is integrated to compress the network structure of CMSCNs for enhanced computational efficiency. Finally, 18 public real datasets and submergence depth data from an oil well are utilized to assess the performance of CMSCNs. The experimental results demonstrate that our proposed method offers improved generalizability and stability while also exhibiting significant potential in practical applications.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323405","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

Reliability-based ordinal consensus adjustment model for large scale group decision making 基于可靠性的大规模群体决策序数共识调整模型

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-09-24 DOI: 10.1016/j.ins.2024.121496

{"title":"Reliability-based ordinal consensus adjustment model for large scale group decision making","authors":"","doi":"10.1016/j.ins.2024.121496","DOIUrl":"10.1016/j.ins.2024.121496","url":null,"abstract":"<div><div>In large scale group decision-making (LSGDM), there are the substantial number of decision makers (DMs) with diverse knowledge, backgrounds, and interests related to the decision-making problem, and it is not possible to assure that all DMs are completely reliable. Thus, in order to enhance the quality of decision-making, it is necessary to analyze the reliabilities of DMs in LSGDM. This paper proposes the method to evaluate the reliabilities of DMs, sorts these DMs according to their degree of reliability, and investigates the consensus reaching process based on categories and an ordinal consensus measure. Considering the DMs' trust network, the uncertainty of a DM's evaluation information represented by a fuzzy preference relation (FPR), the deviation between a DM's FPR and those of the other DMs, and additive consistency of FPRs, the reliability of a DM is assessed using four criteria: PageRank centrality, professional competence, collaborative competence, and additive consistency. Following these reliability assessment criteria, ELECTRE-TRI is employed to sort DMs into three ordered categories according to DMs' different levels of reliability under the four assessment criteria. Furthermore, an improved ordinal consensus measure is designed to consider both the importance weights of positions and the deviation of Borda counts of the same alternative in two rankings. As for the consensus reaching process, due to the varied reliabilities of DMs in different categories, we propose a multiple strategies feedback mechanism for DMs in different categories. Finally, a numerical example is provided to illustrate the rationality and validity of the proposed model.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422813","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

Positivity and semi-global polynomial stability of high-order Cohen–Grossberg BAM neural networks with multiple proportional delays 具有多比例延迟的高阶科恩-格罗斯伯格 BAM 神经网络的正向性和半全局多项式稳定性

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-09-24 DOI: 10.1016/j.ins.2024.121512

{"title":"Positivity and semi-global polynomial stability of high-order Cohen–Grossberg BAM neural networks with multiple proportional delays","authors":"","doi":"10.1016/j.ins.2024.121512","DOIUrl":"10.1016/j.ins.2024.121512","url":null,"abstract":"<div><div>In this paper, we study positivity and semi-global polynomial stability (PS) of higher-order Cohen-Grossberg BAM neural networks with multiple proportional time delays. The proportional delays considered here are unbounded and time-varying, differing from constant, bounded, and distributional time delays. The system model cannot be represented using vector and matrices, making certain approaches within the vector-matrix framework unsuitable for applying. To address this limitation, a direct method based on the solution of the system is proposed to provide sufficient conditions guaranteeing the positivity and semi-global polynomial stability (PS) of the model under consideration. Furthermore, the direct method is applied to establish global PS conditions for BAM neural networks with multiple proportional delays. The obtained conditions contain only a few simple linear scalar inequalities that are easily solved. The applicability of the obtained PS conditions is verified by two numerical examples, and the solution of a linear programming problem is also obtained based on these theoretical results. Notably, this method can be applied to many system models with proportional delays after minor modifications.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323307","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