Cognitive Computation最新文献

{"title":"From Pixels to Prepositions: Linking Visual Perception with Spatial Prepositions Far and Near","authors":"Krishna Raj S R, Srinivasa Chakravarthy V, Anindita Sahoo","doi":"10.1007/s12559-024-10329-6","DOIUrl":"https://doi.org/10.1007/s12559-024-10329-6","url":null,"abstract":"<p>Human language is influenced by sensory-motor experiences. Sensory experiences gathered in a spatiotemporal world are used as raw material to create more abstract concepts. In language, one way to encode spatial relationships is through spatial prepositions. Spatial prepositions that specify the proximity of objects in space, like <i>far</i> and <i>near</i> or their variants, are found in most languages. The mechanism for determining the proximity of another entity to itself is a useful evolutionary trait. From the taxic behavior in unicellular organisms like bacteria to the tropism in the plant kingdom, this behavior can be found in almost all organisms. In humans, vision plays a critical role in spatial localization and navigation. This computational study analyzes the relationship between vision and spatial prepositions using an artificial neural network. For this study, a synthetic image dataset was created, with each image featuring a 2D projection of an object placed in 3D space. The objects can be of various shapes, sizes, and colors. A convolutional neural network is trained to classify the object in the images as <i>far</i> or <i>near</i> based on a set threshold. The study mainly explores two visual scenarios: objects confined to a plane (<b>grounded</b>) and objects not confined to a plane (<b>ungrounded</b>), while also analyzing the influence of camera placement. The classification performance is high for the grounded case, demonstrating that the problem of <i>far/near</i> classification is well-defined for grounded objects, given that the camera is at a sufficient height. The network performance showed that depth can be determined in grounded cases only from monocular cues with high accuracy, given the camera is at an adequate height. The difference in the network’s performance between grounded and ungrounded cases can be explained using the physical properties of the retinal imaging system. The task of determining the distance of an object from individual images in the dataset is challenging as they lack any background cues. Still, the network performance shows the influence of spatial constraints placed on the image generation process in determining depth. The results show that monocular cues significantly contribute to depth perception when all the objects are confined to a single plane. A set of sensory inputs (images) and a specific task (<i>far/near</i> classification) allowed us to obtain the aforementioned results. The visual task, along with reaching and motion, may enable humans to carve the space into various spatial prepositional categories like <i>far</i> and <i>near</i>. The network’s performance and how it learns to classify between <i>far</i> and <i>near</i> provided insights into certain visual illusions that involve size constancy.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"42 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181646","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}

{"title":"Cognitive Analysis of Medical Decision-Making: An Extended MULTIMOORA-Based Multigranulation Probabilistic Model with Evidential Reasoning","authors":"Wenhui Bai, Chao Zhang, Yanhui Zhai, Arun Kumar Sangaiah, Baoli Wang, Wentao Li","doi":"10.1007/s12559-024-10340-x","DOIUrl":"https://doi.org/10.1007/s12559-024-10340-x","url":null,"abstract":"<p>Cognitive computation has leveraged the capabilities of computer algorithms, rendering it an exceptionally efficient approach for addressing multi-attribute group decision-making (MAGDM) problems. Due to the stability of MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) and the capability of evidential reasoning (ER) to combine information from multiple sources, the technique of multigranulation probabilistic rough sets (MG PRSs) holds great promise for solving MAGDM problems. Thus, a new and stable method for MAGDM is proposed. Initially, three forms of multigranulation Pythagorean fuzzy probabilistic rough sets (MG PF PRSs) are constructed using MULTIMOORA approaches. Next, the hierarchical clustering method is employed to cluster similar decision information and consolidate the decision-makers’ preferences. Representatives are chosen from each category to simplify information fusion calculations and reduce complexity by reducing the model’s dimensionality. Following that, the rankings obtained from the three methods are fused using ER. Ultimately, the validity of our method is revealed via a case analysis on chickenpox cases from the UCI data set by employing cognitive analysis. The paper outlines a method for MAGDM that provides significant advantages. Specifically, the use of MULTIMOORA improves the stability of decision results, while the incorporation of ER reduces the overall uncertainty of entire decision processes.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"20 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223810","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}

{"title":"Analyzing Emotional Trends from X Platform Using SenticNet: A Comparative Analysis with Cryptocurrency Price","authors":"Moein Shahiki Tash, Zahra Ahani, Mohim Tash, Olga Kolesnikova, Grigori Sidorov","doi":"10.1007/s12559-024-10335-8","DOIUrl":"https://doi.org/10.1007/s12559-024-10335-8","url":null,"abstract":"<p>This study investigates the relationship between emotional trends derived from X platform data and the market dynamics of prominent cryptocurrencies—Cardano, Binance, Fantom, Matic, and Ripple—during the period from October 2022 to March 2023. Utilizing SenticNet, key emotions such as fear and anxiety, rage and anger, grief and sadness, delight and pleasantness, enthusiasm and eagerness, and delight and joy were identified. The emotional data and cryptocurrency price data, sourced bi-weekly, were analyzed to uncover significant correlations. The findings reveal that emotions such as delight and pleasantness and delight and joy have the strongest positive correlations with Fantom’s price, while delight and pleasantness exhibit the strongest negative correlations with Cardano and Binance. The study highlights the nuanced impact of specific emotional states on cryptocurrency prices, offering valuable insights for market participants.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"303 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141930295","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}

{"title":"Internet of Things for Emotion Care: Advances, Applications, and Challenges","authors":"Xu Xu, Chong Fu, David Camacho, Jong Hyuk Park, Junxin Chen","doi":"10.1007/s12559-024-10327-8","DOIUrl":"https://doi.org/10.1007/s12559-024-10327-8","url":null,"abstract":"","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"49 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141930296","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}

{"title":"Explainable AI for Text Classification: Lessons from a Comprehensive Evaluation of Post Hoc Methods","authors":"Mirko Cesarini, Lorenzo Malandri, Filippo Pallucchini, Andrea Seveso, Frank Xing","doi":"10.1007/s12559-024-10325-w","DOIUrl":"https://doi.org/10.1007/s12559-024-10325-w","url":null,"abstract":"<p>This paper addresses the notable gap in evaluating eXplainable Artificial Intelligence (XAI) methods for text classification. While existing frameworks focus on assessing XAI in areas such as recommender systems and visual analytics, a comprehensive evaluation is missing. Our study surveys and categorises recent post hoc XAI methods according to their scope of explanation and output format. We then conduct a systematic evaluation, assessing the effectiveness of these methods across varying scopes and levels of output granularity using a combination of objective metrics and user studies. Key findings reveal that feature-based explanations exhibit higher fidelity than rule-based ones. While global explanations are perceived as more satisfying and trustworthy, they are less practical than local explanations. These insights enhance understanding of XAI in text classification and offer valuable guidance for developing effective XAI systems, enabling users to evaluate each explainer’s pros and cons and select the most suitable one for their needs.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"71 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141930297","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}

{"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":"<p>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. </p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"56 1","pages":""},"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}

{"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":"<p>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.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"298 1","pages":""},"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}

{"title":"Fermatean Fuzzy Dombi Generalized Maclaurin Symmetric Mean Operators for Prioritizing Bulk Material Handling Technologies","authors":"Abhijit Saha, Svetlana Dabic-Miletic, Tapan Senapati, Vladimir Simic, Dragan Pamucar, Ali Ala, Leena Arya","doi":"10.1007/s12559-024-10323-y","DOIUrl":"https://doi.org/10.1007/s12559-024-10323-y","url":null,"abstract":"","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"20 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872324","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}

{"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":"<p>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.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"352 1","pages":""},"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}

{"title":"Disentangling User Cognitive Intent with Causal Reasoning for Knowledge-Enhanced Recommendation","authors":"Hongcai xu, Junpeng Bao, Qika Lin, Lifang Hou, Feng Chen","doi":"10.1007/s12559-024-10321-0","DOIUrl":"https://doi.org/10.1007/s12559-024-10321-0","url":null,"abstract":"<p>The primary objective of an effective recommender system is to provide accurate, varied, and personalized recommendations that align with the user’s cognitive intents. Given their ability to represent structural and semantic information effectively, knowledge graphs (KGs) are increasingly being utilized to capture auxiliary information for recommendation systems. This trend is supported by the recent advancements in graph neural network (GNN)-based models for KG-aware recommendations. However, these models often struggle with issues such as insufficient user-item interactions and the misalignment of user intent weights during information propagation. Additionally, they face a popularity bias, which is exacerbated by the disproportionate influence of a small number of highly active users and the limited auxiliary information about items. This bias significantly curtails the effectiveness of the recommendations. To address this issue, we propose a Knowledge-Enhanced User Cognitive Intent Network (KeCAIN), which incorporates item category information to capture user intents with information aggregation and eliminate popularity bias based on causal reasoning in recommendation systems. Experiments on three real-world datasets show that KeCAIN outperforms state-of-the-art baselines.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"4 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141744867","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}