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Search Results (239)

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Keywords = cognitive graphs

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23 pages, 9496 KB  
Article
Symmetry-Aware LSTM-Based Effective Connectivity Framework for Identifying MCI Progression and Reversion with Resting-State fMRI
by Bowen Sun, Lei Wang, Mengqi Gao, Ziyu Fan and Tongpo Zhang
Symmetry 2025, 17(10), 1754; https://doi.org/10.3390/sym17101754 - 17 Oct 2025
Viewed by 116
Abstract
Mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer’s disease (AD), comprises three potential trajectories: reversion, stability, or progression. Accurate prediction of these trajectories is crucial for disease modeling and early intervention. We propose a novel analytical framework that integrates [...] Read more.
Mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer’s disease (AD), comprises three potential trajectories: reversion, stability, or progression. Accurate prediction of these trajectories is crucial for disease modeling and early intervention. We propose a novel analytical framework that integrates a healthy control–AD difference template (HAD) with a large-scale Granger causality algorithm based on long short-term memory networks (LSTM-lsGC) to construct effective connectivity (EC) networks. By applying principal component analysis for dimensionality reduction, modeling dynamic sequences with LSTM, and estimating EC matrices through Granger causality, the framework captures both symmetrical and asymmetrical connectivity, providing a refined characterization of the network alterations underlying MCI progression and reversion. Leveraging graph-theoretical features, our method achieved an MCI subtype classification accuracy of 84.92% (AUC = 0.84) across three subgroups and 90.86% when distinguishing rMCI from pMCI. Moreover, key brain regions, including the precentral gyrus, hippocampus, and cerebellum, were identified as being associated with MCI progression. Overall, by developing a symmetry-aware effective connectivity framework that simultaneously investigates both MCI progression and reversion, this study bridges a critical gap and offers a promising tool for early detection and dynamic disease characterization. Full article
(This article belongs to the Section Computer)
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27 pages, 5279 KB  
Article
Concept-Guided Exploration: Building Persistent, Actionable Scene Graphs
by Noé José Zapata Cornejo, Gerardo Pérez, Alejandro Torrejón, Pedro Núñez and Pablo Bustos
Appl. Sci. 2025, 15(20), 11084; https://doi.org/10.3390/app152011084 - 16 Oct 2025
Viewed by 110
Abstract
The perception of 3D space by mobile robots is rapidly moving from flat metric grid representations to hybrid metric-semantic graphs built from human-interpretable concepts. While most approaches first build metric maps and then add semantic layers, we explore an alternative, concept-first architecture in [...] Read more.
The perception of 3D space by mobile robots is rapidly moving from flat metric grid representations to hybrid metric-semantic graphs built from human-interpretable concepts. While most approaches first build metric maps and then add semantic layers, we explore an alternative, concept-first architecture in which spatial understanding emerges from asynchronous concept agents that directly instantiate and manage semantic entities. Our robot employs two spatial concepts—room and door—implemented as autonomous processes within a cognitive distributed architecture. These concept agents cooperatively build a shared scene graph representation of indoor layouts through active exploration and incremental validation. The key architectural principle is hierarchical constraint propagation: Room instantiation provides geometric and semantic priors to guide and support door detection within wall boundaries. The resulting structure is maintained by a complementary functional principle based on prediction-matching loops. This approach is designed to yield an actionable, human-interpretable spatial representation without relying on any pre-existing global metric map, supporting scalable operation and persistent, task-relevant understanding in structured indoor environments. Full article
(This article belongs to the Special Issue Advances in Cognitive Robotics and Control)
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25 pages, 1839 KB  
Article
Modeling the Emergence of Insight via Quantum Interference on Semantic Graphs
by Arianna Pavone and Simone Faro
Mathematics 2025, 13(19), 3171; https://doi.org/10.3390/math13193171 - 3 Oct 2025
Viewed by 182
Abstract
Creative insight is a core phenomenon of human cognition, often characterized by the sudden emergence of novel and contextually appropriate ideas. Classical models based on symbolic search or associative networks struggle to capture the non-linear, context-sensitive, and interference-driven aspects of insight. In this [...] Read more.
Creative insight is a core phenomenon of human cognition, often characterized by the sudden emergence of novel and contextually appropriate ideas. Classical models based on symbolic search or associative networks struggle to capture the non-linear, context-sensitive, and interference-driven aspects of insight. In this work, we propose a computational model of insight generation grounded in continuous-time quantum walks over weighted semantic graphs, where nodes represent conceptual units and edges encode associative relationships. By exploiting the principles of quantum superposition and interference, the model enables the probabilistic amplification of semantically distant but contextually relevant concepts, providing a plausible account of non-local transitions in thought. The model is implemented using standard Python 3.10 libraries and is available both as an interactive fully reproducible Google Colab notebook and a public repository with code and derived datasets. Comparative experiments on ConceptNet-derived subgraphs, including the Candle Problem, 20 Remote Associates Test triads, and Alternative Uses, show that, relative to classical diffusion, quantum walks concentrate more probability on correct targets (higher AUC and peaks reached earlier) and, in open-ended settings, explore more broadly and deeply (higher entropy and coverage, larger expected radius, and faster access to distant regions). These findings are robust under normalized generators and a common time normalization, align with our formal conditions for transient interference-driven amplification, and support quantum-like dynamics as a principled process model for key features of insight. Full article
(This article belongs to the Section E1: Mathematics and Computer Science)
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15 pages, 1072 KB  
Article
Balancing Layout Space and Risk Comprehension in Health Communication: A Comparison of Separated and Integrated Icon Arrays
by Li-Jen Wang and Meng-Cong Zheng
Informatics 2025, 12(4), 105; https://doi.org/10.3390/informatics12040105 - 30 Sep 2025
Viewed by 464
Abstract
This study investigated how icon array layouts influence comprehension of medical risk information, particularly in relation to users’ cognitive abilities. In a within-subjects experiment (N = 121), participants reviewed clinical scenarios with treatment-related risks and side effect risks displayed in either separated or [...] Read more.
This study investigated how icon array layouts influence comprehension of medical risk information, particularly in relation to users’ cognitive abilities. In a within-subjects experiment (N = 121), participants reviewed clinical scenarios with treatment-related risks and side effect risks displayed in either separated or integrated icon arrays. Comprehension was significantly higher for separated treatment-related risk layouts (p < 0.001), while side effect layout showed no effect. Numeracy and graph literacy significantly predicted comprehension. Crucially, individuals with lower numeracy showed marked gains when viewing separated formats, whereas those with higher numeracy performed well regardless of layout. Despite this, participants preferred hybrid formats—separated treatment-related risk with integrated side effect risks—revealing a critical preference–performance gap. By demonstrating how visual layout interacts with user abilities, this study provides actionable guidance for patient decision aid design. The findings show that comprehension accuracy must take precedence over layout compactness and user preference, with separated layouts recommended for treatment-related risks—especially for individuals with lower numeracy—and greater flexibility allowed for side effect risks when space is limited. Full article
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34 pages, 1982 KB  
Article
Knowledge Graphs and Artificial Intelligence for the Implementation of Cognitive Heritage Digital Twins
by Achille Felicetti, Aida Himmiche and Miriana Somenzi
Appl. Sci. 2025, 15(18), 10061; https://doi.org/10.3390/app151810061 - 15 Sep 2025
Viewed by 1262
Abstract
This paper explores the integration of Artificial Intelligence and semantic technologies to support the creation of intelligent Heritage Digital Twins, digital constructs capable of representing, interpreting, and reasoning over cultural data. This study focuses on transforming the often fragmented and unstructured documentation produced [...] Read more.
This paper explores the integration of Artificial Intelligence and semantic technologies to support the creation of intelligent Heritage Digital Twins, digital constructs capable of representing, interpreting, and reasoning over cultural data. This study focuses on transforming the often fragmented and unstructured documentation produced in cultural heritage into coherent Knowledge Graphs aligned with internationally recognised standards and ontologies. Two complementary AI-assisted workflows are proposed: one for extracting and formalising structured knowledge from heritage science reports and another for enhancing AI models through the integration of curated ontological knowledge. The experiments demonstrate how this synergy facilitates both the retrieval and the reuse of complex information while ensuring interpretability and semantic consistency. Beyond technical efficacy, this paper also addresses the ethical implications of AI use in cultural heritage, with particular attention to transparency, bias mitigation, and meaningful representation of diverse narratives. The results highlight the importance of a reflexive and ethically grounded deployment of AI, where knowledge extraction and machine learning are guided by structured ontologies and human oversight, to ensure conceptual rigour and respect for cultural complexity. Full article
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13 pages, 1096 KB  
Article
Effect of the Virtual Reality-Infused Movement and Activity Program (V-MAP) on Physical Activity and Cognition in Head Start Preschoolers
by Xiangli Gu, Samantha Moss, Xiaoxia Zhang, Tao Zhang and Tracy L. Greer
Children 2025, 12(9), 1228; https://doi.org/10.3390/children12091228 - 14 Sep 2025
Viewed by 797
Abstract
Background/Objectives: This study examined the efficacy of a physical activity (PA) intervention augmented by a non-immersive Virtual Reality (VR) gaming system (i.e., Virtual Reality-infused Movement and Activity Program; V-MAP) on physical activity (i.e., sedentary behavior, moderate-to-vigorous PA [MVPA], vigorous PA [VPA]) and cognitive [...] Read more.
Background/Objectives: This study examined the efficacy of a physical activity (PA) intervention augmented by a non-immersive Virtual Reality (VR) gaming system (i.e., Virtual Reality-infused Movement and Activity Program; V-MAP) on physical activity (i.e., sedentary behavior, moderate-to-vigorous PA [MVPA], vigorous PA [VPA]) and cognitive skills (i.e., response error, movement latency and reaction time) in Head Start preschoolers. Methods: Using a repeated-measure with 1-month follow-up design, a sample of 13 Head Start preschoolers (Mage = 67.08 ± 4.32 months; 36.2% boys) engaged in a 6-week V-MAP intervention (30-min session; 8 sessions) that focused on non-immersive VR based movement integration. The Cambridge Neuropsychological Test Automated Battery (CANTAB) was used to measure cognition; school-based PA and sedentary behavior were assessed by ActiGraph accelerometer. Pedometers were used to monitor real time engagement and implementation over eight intervention sessions. Results: On average, children obtained 1105 steps during the 30-min intervention (36.85 steps/min). There was a significant increase in VPA after the V-MAP intervention, whereas no significant changes in MVPA or sedentary behavior were observed (ps > 0.05). Although we did not observe significant improvement in studied cognitive function variables (ps > 0.05) after the V-MAP intervention, some delayed effects were observed in the follow-up test (Cohen’s d ranges from −0.41 to −0.73). Conclusions: This efficacy trial provides preliminary support that implementing V-MAP in recess may help Head Start preschoolers achieve or accumulate the recommended daily 60-min MVPA guideline during preschool years. The findings also provide insights that VR-based PA for as little as 30 min per day may benefit cognitive capability. Full article
(This article belongs to the Section Global Pediatric Health)
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26 pages, 3998 KB  
Article
Graph-Symmetry Cognitive Learning for Multi-Scale Cloud Imaging: An Uncertainty-Quantified Geometric Paradigm via Hierarchical Graph Networks
by Qing Xu, Zichen Zhang, Guanfang Wang and Yunjie Chen
Symmetry 2025, 17(9), 1477; https://doi.org/10.3390/sym17091477 - 7 Sep 2025
Viewed by 438
Abstract
Cloud imagery analysis from terrestrial observation points represents a fundamental capability within contemporary atmospheric monitoring infrastructure, serving essential functions in meteorological prediction, climatic surveillance, and hazard alert systems. However, traditional ground-based cloud image segmentation methods have fundamental limitations, particularly their inability to effectively [...] Read more.
Cloud imagery analysis from terrestrial observation points represents a fundamental capability within contemporary atmospheric monitoring infrastructure, serving essential functions in meteorological prediction, climatic surveillance, and hazard alert systems. However, traditional ground-based cloud image segmentation methods have fundamental limitations, particularly their inability to effectively model the graph structure and symmetry in cloud data. To address this, we propose G-CLIP, a ground-based cloud image segmentation method based on graph symmetry. G-CLIP synergistically integrates four innovative modules. First, the Prototype-Driven Asymmetric Attention (PDAA) module is designed to reduce complexity and enhance feature learning by leveraging permutation invariance and graph symmetry principles. Second, the Symmetry-Adaptive Graph Convolution Layer (SAGCL) is constructed, modeling pixels as graph nodes, using cosine similarity to build a sparse discriminative structure, and ensuring stability through symmetry and degree normalization. Third, the Multi-Scale Directional Edge Optimizer (MSDER) is developed to explicitly model complex symmetric relationships in cloud features from a graph theory perspective. Finally, the Uncertainty-Driven Loss Optimizer (UDLO) is proposed to dynamically adjust weights to address foreground–background imbalance and provide uncertainty quantification. Extensive experiments on four benchmark datasets demonstrate that our method achieves state-of-the-art performance across all evaluation metrics. Our work provides a novel theoretical framework and practical solution for applying graph neural networks (GNNs) to meteorology, particularly by integrating graph properties with uncertainty and leveraging symmetries from graph theory for complex spatial modeling. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry Study in Graph Theory)
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17 pages, 1234 KB  
Article
Co-Designing a DSM-5-Based AI-Powered Smart Assistant for Monitoring Dementia and Ongoing Neurocognitive Decline: Development Study
by Fareed Ud Din, Nabaraj Giri, Namrata Shetty, Tom Hilton, Niusha Shafiabady and Phillip J. Tully
BioMedInformatics 2025, 5(3), 49; https://doi.org/10.3390/biomedinformatics5030049 - 2 Sep 2025
Viewed by 1262
Abstract
Background/Objectives: Dementia is a leading cause of cognitive decline, with significant challenges for early detection and timely intervention. The lack of effective, user-centred technologies further limits clinical response, particularly in underserved areas. This study aimed to develop and describe a co-design process for [...] Read more.
Background/Objectives: Dementia is a leading cause of cognitive decline, with significant challenges for early detection and timely intervention. The lack of effective, user-centred technologies further limits clinical response, particularly in underserved areas. This study aimed to develop and describe a co-design process for creating a Diagnostic and Statistical Manual of Mental Disorders (DSM-5)-compliant, AI-powered Smart Assistant (SmartApp) to monitor neurocognitive decline, while ensuring accessibility, clinical relevance, and responsible AI integration. Methods: A co-design framework was applied using a novel combination of Agile principles and the Double Diamond Model (DDM). More than twenty iterative Scrum sprints were conducted, involving key stakeholders such as clinicians (psychiatrist, psychologist, physician), designers, students, and academic researchers. Prototype testing and design workshops were organised to gather structured feedback. Feedback was systematically incorporated into subsequent iterations to refine functionality, usability, and clinical applicability. Results: The iterative process resulted in a SmartApp that integrates a DSM-5-based screening tool with 24 items across key cognitive domains. Key features include longitudinal tracking of cognitive performance, comparative visual graphs, predictive analytics using a regression-based machine learning module, and adaptive user interfaces. Workshop participants reported high satisfaction with features such as simplified navigation, notification reminders, and clinician-focused reporting modules. Conclusions: The findings suggest that combining co-design methods with Agile/DDM frameworks provides an effective pathway for developing AI-powered clinical tools as per responsible AI standards. The SmartApp offers a clinically relevant, user-friendly platform for dementia screening and monitoring, with potential to support vulnerable populations through scalable, responsible digital health solutions. Full article
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36 pages, 7369 KB  
Article
Ontology-Driven Digital Twin Framework for Aviation Maintenance and Operations
by Igor Kabashkin
Mathematics 2025, 13(17), 2817; https://doi.org/10.3390/math13172817 - 2 Sep 2025
Viewed by 959
Abstract
This paper presents a novel ontology-driven digital twin framework specifically designed for aviation maintenance and operations that addresses these challenges through semantic reasoning and explainable decision support. The proposed framework integrates seven interconnected ontologies—structural, functional, behavioral, monitoring, maintenance, lifecycle, and environmental. It collectively [...] Read more.
This paper presents a novel ontology-driven digital twin framework specifically designed for aviation maintenance and operations that addresses these challenges through semantic reasoning and explainable decision support. The proposed framework integrates seven interconnected ontologies—structural, functional, behavioral, monitoring, maintenance, lifecycle, and environmental. It collectively provides a comprehensive semantic representation of aircraft systems and their operational context. Each ontology is mathematically formalized using description logics and graph theory, creating a unified knowledge graph that enables transparent, traceable reasoning from sensor observations to maintenance decisions. The digital twin is formally defined as a 6-tuple that incorporates semantic transformation engines, cross-ontology mappings, and dynamic reasoning mechanisms. Unlike traditional data-driven approaches that operate as black boxes, the ontology-driven framework provides explainable inference capabilities essential for regulatory compliance and safety certification in aviation. The semantic foundation enables causal reasoning, rule-based validation, and context-aware maintenance recommendations while supporting standardization and interoperability across manufacturers, airlines, and regulatory bodies. The research contributes a mathematically grounded, semantically transparent framework that bridges the gap between domain knowledge and operational data in aviation maintenance. This work establishes the foundation for next-generation cognitive maintenance systems that can support intelligent, adaptive, and trustworthy operations in modern aviation ecosystems. Full article
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19 pages, 6998 KB  
Article
EEG-Based Fatigue Detection for Remote Tower Air Traffic Controllers Using a Spatio-Temporal Graph with Center Loss Network
by Linfeng Zhong, Peilin Luo, Ruohui Hu, Qingwei Zhong, Qinghai Zuo, Youyou Li, Yi Ai and Weijun Pan
Aerospace 2025, 12(9), 786; https://doi.org/10.3390/aerospace12090786 - 29 Aug 2025
Viewed by 559
Abstract
Fatigue in air traffic controllers (ATCOs), particularly within remote tower operations, poses a substantial risk to aviation safety due to its detrimental effects on vigilance, decision-making, and situational awareness. While electroencephalography (EEG) provides a promising avenue for objective fatigue monitoring, existing models often [...] Read more.
Fatigue in air traffic controllers (ATCOs), particularly within remote tower operations, poses a substantial risk to aviation safety due to its detrimental effects on vigilance, decision-making, and situational awareness. While electroencephalography (EEG) provides a promising avenue for objective fatigue monitoring, existing models often fail to adequately capture both the spatial dependencies across brain regions and the temporal dynamics of cognitive states. To address this challenge, we propose a novel EEG-based fatigue detection framework, Spatio-Temporal Graph with Center Loss Network (STG-CLNet), which jointly models topological brain connectivity and temporal EEG evolution. The model leverages a multi-stage graph convolutional network to encode spatial dependencies and a triple-layer LSTM module to capture temporal progression, while incorporating center loss to enhance feature discriminability in the embedding space. We constructed a domain-specific EEG dataset involving 34 ATCO participants operating in high- and low-traffic remote tower simulations, with fatigue labels derived from three validated subjective metrics. Experimental results demonstrate that STG-CLNet achieves superior classification performance (accuracy = 96.73%, recall = 92.01%, F1-score = 87.15%), outperforming several strong baselines, including LSTM and EEGNet. These findings underscore the potential of STG-CLNet for integration into real-time cognitive monitoring systems in air traffic control, contributing to both theoretical advancement and operational safety enhancement. Full article
(This article belongs to the Section Air Traffic and Transportation)
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17 pages, 1236 KB  
Article
Resilient Software Design Through Cognitive-Aware Antipattern Propagation in 4+1 Architectural Views
by Roberto Andrade, Jenny Torres, Iván Ortiz-Garcés and Jorge Segovia
Appl. Sci. 2025, 15(17), 9526; https://doi.org/10.3390/app15179526 - 29 Aug 2025
Viewed by 533
Abstract
This paper proposes a formal framework to model the propagation of software antipatterns across architectural layers, quantifying their impact using principles from complex systems theory, technical debt economics, and cognitive load theory. By extending the 4+1 architectural view model with a propagation graph [...] Read more.
This paper proposes a formal framework to model the propagation of software antipatterns across architectural layers, quantifying their impact using principles from complex systems theory, technical debt economics, and cognitive load theory. By extending the 4+1 architectural view model with a propagation graph and economic simulation, the proposed framework enables software teams to predict, visualize, and mitigate the systemic effects of structural faults. We support our proposal with a mathematical model, a conceptual propagation engine, and simulation results Full article
(This article belongs to the Special Issue Cyber Security and Software Engineering)
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17 pages, 3343 KB  
Article
PB Space: A Mathematical Framework for Modeling Presence and Implication Balance in Psychological Change Through Fuzzy Cognitive Maps
by Alejandro Sanfeliciano, Luis Angel Saúl, Carlos Hurtado-Martínez and Luis Botella
Axioms 2025, 14(9), 650; https://doi.org/10.3390/axioms14090650 - 22 Aug 2025
Cited by 1 | Viewed by 565
Abstract
Understanding psychological change requires a quantitative framework capable of capturing the complex and dynamic relationships among personal constructs. Personal Construct Psychology emphasizes the hierarchical reorganization of bipolar constructs, yet existing qualitative methods inadequately model the reciprocal and graded influences involved in such change. [...] Read more.
Understanding psychological change requires a quantitative framework capable of capturing the complex and dynamic relationships among personal constructs. Personal Construct Psychology emphasizes the hierarchical reorganization of bipolar constructs, yet existing qualitative methods inadequately model the reciprocal and graded influences involved in such change. This paper introduces the Presence–Balance (PB) space, a centrality measure for constructs represented within Fuzzy Cognitive Maps (FCMs). FCMs model cognitive systems as directed, weighted graphs, allowing for nuanced analysis of construct interactions. The PB space operationalizes two orthogonal dimensions: Presence, representing the overall connectivity and activation of a construct, and Implication Balance, quantifying the directional asymmetry between influences exerted and received. By formalizing Hinkle’s hierarchical theory within a rigorous mathematical framework, the PB space enables precise identification of constructs that drive or resist transformation. This dual-dimensional model provides a structured method for analyzing personal construct systems, supporting both theoretical exploration and clinically relevant interpretations in the study of psychological change. Full article
(This article belongs to the Special Issue Recent Advances in Fuzzy Theory Applications)
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20 pages, 1818 KB  
Article
Image Captioning Model Based on Multi-Step Cross-Attention Cross-Modal Alignment and External Commonsense Knowledge Augmentation
by Liang Wang, Meiqing Jiao, Zhihai Li, Mengxue Zhang, Haiyan Wei, Yuru Ma, Honghui An, Jiaqi Lin and Jun Wang
Electronics 2025, 14(16), 3325; https://doi.org/10.3390/electronics14163325 - 21 Aug 2025
Viewed by 1038
Abstract
To address the semantic mismatch between limited textual descriptions in image captioning training datasets and the multi-semantic nature of images, as well as the underutilized external commonsense knowledge, this article proposes a novel image captioning model based on multi-step cross-attention cross-modal alignment and [...] Read more.
To address the semantic mismatch between limited textual descriptions in image captioning training datasets and the multi-semantic nature of images, as well as the underutilized external commonsense knowledge, this article proposes a novel image captioning model based on multi-step cross-attention cross-modal alignment and external commonsense knowledge enhancement. The model employs a backbone architecture comprising CLIP’s ViT visual encoder, Faster R-CNN, BERT text encoder, and GPT-2 text decoder. It incorporates two core mechanisms: a multi-step cross-attention mechanism that iteratively aligns image and text features across multiple rounds, progressively enhancing inter-modal semantic consistency for more accurate cross-modal representation fusion. Moreover, the model employs Faster R-CNN to extract region-based object features. These features are mapped to corresponding entities within the dataset through entity probability calculation and entity linking. External commonsense knowledge associated with these entities is then retrieved from the ConceptNet knowledge graph, followed by knowledge embedding via TransE and multi-hop reasoning. Finally, the fused multimodal features are fed into the GPT-2 decoder to steer caption generation, enhancing the lexical richness, factual accuracy, and cognitive plausibility of the generated descriptions. In the experiments, the model achieves CIDEr scores of 142.6 on MSCOCO and 78.4 on Flickr30k. Ablations confirm both modules enhance caption quality. Full article
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25 pages, 7961 KB  
Article
A Multi-Layer Attention Knowledge Tracking Method with Self-Supervised Noise Tolerance
by Haifeng Wang, Hao Liu, Yanling Ge and Zhihao Yu
Appl. Sci. 2025, 15(15), 8717; https://doi.org/10.3390/app15158717 - 6 Aug 2025
Viewed by 642
Abstract
The knowledge tracing method based on deep learning is used to assess learners’ cognitive states, laying the foundation for personalized education. However, deep learning methods are inefficient when processing long-term series data and are prone to overfitting. To improve the accuracy of cognitive [...] Read more.
The knowledge tracing method based on deep learning is used to assess learners’ cognitive states, laying the foundation for personalized education. However, deep learning methods are inefficient when processing long-term series data and are prone to overfitting. To improve the accuracy of cognitive state prediction, we design a Multi-layer Attention Self-supervised Knowledge Tracing Method (MASKT) using self-supervised learning and the Transformer method. In the pre-training stage, MASKT uses a random forest method to filter out positive and negative correlation feature embeddings; then, it reuses noise-processed restoration tasks to extract more learnable features and enhance the learning ability of the model. The Transformer in MASKT not only solves the problem of long-term dependencies between input and output using an attention mechanism, but also has parallel computing capabilities that can effectively improve the learning efficiency of the prediction model. Finally, a multidimensional attention mechanism is integrated into cross-attention to further optimize prediction performance. The experimental results show that, compared with various knowledge tracing models on multiple datasets, MASKT’s prediction performance remains 2 percentage points higher. Compared with the multidimensional attention mechanism of graph neural networks, MASKT’s time efficiency is shortened by nearly 30%. Due to the improvement in prediction accuracy and performance, this method has broad application prospects in the field of cognitive diagnosis in intelligent education. Full article
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28 pages, 15658 KB  
Article
Unifying Flood-Risk Communication: Empowering Community Leaders Through AI-Enhanced, Contextualized Storytelling
by Michal Zajac, Connor Kulawiak, Shenglin Li, Caleb Erickson, Nathan Hubbell and Jiaqi Gong
Hydrology 2025, 12(8), 204; https://doi.org/10.3390/hydrology12080204 - 4 Aug 2025
Cited by 1 | Viewed by 1221
Abstract
Floods pose a growing threat globally, causing tragic loss of life, billions in economic damage annually, and disproportionately affecting socio-economically vulnerable populations. This paper aims to improve flood-risk communication for community leaders by exploring the application of artificial intelligence. We categorize U.S. flood [...] Read more.
Floods pose a growing threat globally, causing tragic loss of life, billions in economic damage annually, and disproportionately affecting socio-economically vulnerable populations. This paper aims to improve flood-risk communication for community leaders by exploring the application of artificial intelligence. We categorize U.S. flood information sources, review communication modalities and channels, synthesize the literature on community leaders’ roles in risk communication, and analyze existing technological tools. Our analysis reveals three key challenges: the fragmentation of flood information, information overload that impedes decision-making, and the absence of a unified communication platform to address these issues. We find that AI techniques can organize data and significantly enhance communication effectiveness, particularly when delivered through infographics and social media channels. Based on these findings, we propose FLAI (Flood Language AI), an AI-driven flood communication platform that unifies fragmented flood data sources. FLAI employs knowledge graphs to structure fragmented data sources and utilizes a retrieval-augmented generation (RAG) framework to enable large language models (LLMs) to produce contextualized narratives, including infographics, maps, and cost–benefit analyses. Beyond flood management, FLAI’s framework demonstrates how AI can transform public service data management and institutional AI readiness. By centralizing and organizing information, FLAI can significantly reduce the cognitive burden on community leaders, helping them communicate timely, actionable insights to save lives and build flood resilience. Full article
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