Cognitive Computation最新文献_第3页

Prompt Learning for Multimodal Intent Recognition with Modal Alignment Perception 利用模态对齐感知进行多模态意图识别的提示学习

IF 4.3 3区计算机科学

Cognitive Computation Pub Date : 2024-08-10 DOI: 10.1007/s12559-024-10328-7

Yuzhao Chen, Wenhua Zhu, Weilun Yu, Hongfei Xue, Hao Fu, Jiali Lin, Dazhi Jiang

引用次数: 0

Prompt Learning for Multimodal Intent Recognition with Modal Alignment Perception 利用模态对齐感知进行多模态意图识别的提示学习

IF 4.3 3区计算机科学

Cognitive Computation Pub Date : 2024-08-10 DOI: 10.1007/s12559-024-10328-7

Yuzhao Chen, Wenhua Zhu, Weilun Yu, Hongfei Xue, Hao Fu, Jiali Lin, Dazhi Jiang

引用次数: 0

Analyzing Emotional Trends from X Platform Using SenticNet: A Comparative Analysis with Cryptocurrency Price 使用 SenticNet 分析来自 X 平台的情感趋势：与加密货币价格的对比分析

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-08-09 DOI: 10.1007/s12559-024-10335-8

Moein Shahiki Tash, Zahra Ahani, Mohim Tash, Olga Kolesnikova, Grigori Sidorov

引用次数: 0

Internet of Things for Emotion Care: Advances, Applications, and Challenges 情感护理物联网：进展、应用与挑战

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-08-07 DOI: 10.1007/s12559-024-10327-8

Xu Xu, Chong Fu, David Camacho, Jong Hyuk Park, Junxin Chen

引用次数: 0

Explainable AI for Text Classification: Lessons from a Comprehensive Evaluation of Post Hoc Methods 用于文本分类的可解释人工智能：后发方法综合评估的启示

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-08-06 DOI: 10.1007/s12559-024-10325-w

Mirko Cesarini, Lorenzo Malandri, Filippo Pallucchini, Andrea Seveso, Frank Xing

引用次数: 0

Cognitive-Inspired Deep Learning Models for Aspect-Based Sentiment Analysis: A Retrospective Overview and Bibliometric Analysis 用于基于方面的情感分析的认知启发深度学习模型：回顾性概述和文献计量分析

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-08-06 DOI: 10.1007/s12559-024-10331-y

Xieling Chen, Haoran Xie, S. Joe Qin, Yaping Chai, Xiaohui Tao, Fu Lee Wang

{"title":"Cognitive-Inspired Deep Learning Models for Aspect-Based Sentiment Analysis: A Retrospective Overview and Bibliometric Analysis","authors":"Xieling Chen, Haoran Xie, S. Joe Qin, Yaping Chai, Xiaohui Tao, Fu Lee Wang","doi":"10.1007/s12559-024-10331-y","DOIUrl":"https://doi.org/10.1007/s12559-024-10331-y","url":null,"abstract":"As cognitive-inspired computation approaches, deep neural networks or deep learning (DL) models have played important roles in allowing machines to reach human-like performances in various complex cognitive tasks such as cognitive computation and sentiment analysis. This paper offers a thorough examination of the rapidly developing topic of DL-assisted aspect-based sentiment analysis (DL-ABSA), focusing on its increasing importance and implications for practice and research advancement. Leveraging bibliometric indicators, social network analysis, and topic modeling techniques, the study investigates four research questions: publication and citation trends, scientific collaborations, major themes and topics, and prospective research directions. The analysis reveals significant growth in DL-ABSA research output and impact, with notable contributions from diverse publication sources, institutions, and countries/regions. Collaborative networks between countries/regions, particularly between the USA and China, underscore global engagement in DL-ABSA research. Major themes such as syntax and structure analysis, neural networks for sequence modeling, and specific aspects and modalities in sentiment analysis emerge from the analysis, guiding future research endeavors. The study identifies prospective avenues for practitioners, emphasizing the strategic importance of syntax analysis, neural network methodologies, and domain-specific applications. Overall, this study contributes to the understanding of DL-ABSA research dynamics, providing a roadmap for practitioners and researchers to navigate the evolving landscape and drive innovations in DL-ABSA methodologies and applications. ","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141930298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Consensus Model with Non-Cooperative Behavior Adaptive Management Based on Cognitive Psychological State Computation in Large-Scale Group Decision 基于大规模群体决策中认知心理状态计算的非合作行为适应性管理共识模型

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-08-02 DOI: 10.1007/s12559-024-10330-z

Yuetong Chen, Mingrui Zhou, Fengming Liu

{"title":"A Consensus Model with Non-Cooperative Behavior Adaptive Management Based on Cognitive Psychological State Computation in Large-Scale Group Decision","authors":"Yuetong Chen, Mingrui Zhou, Fengming Liu","doi":"10.1007/s12559-024-10330-z","DOIUrl":"https://doi.org/10.1007/s12559-024-10330-z","url":null,"abstract":"Social cognition proposed that individual cognitive psychology was closely related to decision-making behavior. The heterogeneity of individual cognitive psychology has been ignored in large-scale decision-making. This research proposes a novel consensus decision model based on cognitive psychological state computation. Effective trust, cognitive trust, and opinion similarity are integrated to construct a fusion relationship network, and Louvain algorithm is used to divide communities. On this basis, non-cooperative individuals are identified. We quantify and classify individual cognitive psychological states by introducing attitude-belief factors. In this process, the cognitive trust and cognitive expression involved have fuzziness and uncertainty, which are quantified and computed by intuitionistic fuzzy set theory. Considering the difference in cognitive dissonance among non-cooperative individuals with different cognitive states, an adaptive feedback mechanism and trust renewal rule are proposed. The simulation results show that, on the one hand, the consensus model in this paper has a high timeliness. On the other hand, among the four types of cognitive psychological state, the non-cooperative individual with higher attitude factor and lower belief factor had higher management efficiency and consensus-reaching speed.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Fermatean Fuzzy Dombi Generalized Maclaurin Symmetric Mean Operators for Prioritizing Bulk Material Handling Technologies 用于确定散装物料处理技术优先次序的 Fermatean Fuzzy Dombi 广义 Maclaurin 对称均值算子

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-31 DOI: 10.1007/s12559-024-10323-y

Abhijit Saha, Svetlana Dabic-Miletic, Tapan Senapati, Vladimir Simic, Dragan Pamucar, Ali Ala, Leena Arya

引用次数: 0

Multi-View Cooperative Learning with Invariant Rationale for Document-Level Relation Extraction 利用不变原理进行文档级关系提取的多视图合作学习

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-27 DOI: 10.1007/s12559-024-10322-z

Rui Lin, Jing Fan, Yinglong He, Yehui Yang, Jia Li, Cunhan Guo

{"title":"Multi-View Cooperative Learning with Invariant Rationale for Document-Level Relation Extraction","authors":"Rui Lin, Jing Fan, Yinglong He, Yehui Yang, Jia Li, Cunhan Guo","doi":"10.1007/s12559-024-10322-z","DOIUrl":"https://doi.org/10.1007/s12559-024-10322-z","url":null,"abstract":"Document-level relation extraction (RE) is a complex and significant natural language processing task, as the massive entity pairs exist in the document and are across sentences in reality. However, the existing relation extraction methods (deep learning) often use single-view information (e.g., entity-level or sentence-level) to learn the relational information but ignore the multi-view information, and the explanations of deep learning are difficult to be reflected, although it achieves good results. To extract high-quality relational information from the document and improve the explanations of the model, we propose a multi-view cooperative learning with invariant rationale (MCLIR) framework. Firstly, we design the multi-view cooperative learning to find latent relational information from the various views. Secondly, we utilize invariant rationale to encourage the model to focus on crucial information, which can empower the performance and explanations of the model. We conduct the experiment on two public datasets, and the results of the experiment demonstrate the effectiveness of MCLIR.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Editorial: What AI and Neuroscience Can Learn from Each Other—Open Problems in Models and Theories 社论：人工智能和神经科学可以相互学习什么--模型和理论中的未决问题

IF 4.3 3区计算机科学

Cognitive Computation Pub Date : 2024-07-23 DOI: 10.1007/s12559-024-10324-x

Asim Roy, A. Minai, J. Thivierge, Tsvi Achler, Juyang Weng

引用次数: 0