Confusion is a common emotion during the learning process, and its recognition is critical for evaluating knowledge acquisition. therefore, evaluating learners’ confusion states is crucial. Facial expressions, as dir...
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ISBN:
(数字)9798331544577
ISBN:
(纸本)9798331544584
Confusion is a common emotion during the learning process, and its recognition is critical for evaluating knowledge acquisition. therefore, evaluating learners’ confusion states is crucial. Facial expressions, as direct indicators of emotional states, are widely used in emotion analysis. However, most existing research focuses on basic emotions, with limited attention given to learning emotions. Estimating confusion intensity presents two key challenges: (1) Confusion often occurs in long sequences; and (2) Confusion exhibits significant individual variability. To address these challenges, we propose three key innovations: First, we introduce an unsupervised method for extracting confusion frames based on its unique characteristics. Second, we employ a Masked Autoencoder (MAE) to capture implicit features and retain relevant external cues of confusion. third, we design an Action Unit-assisted Graph Convolutional Network (GCN) to leverage subtle facial muscle movements, establishing relationships between samples and mitigating individual differences. Experiments on the public dataset show that our method effectively estimates the intensity of the learners’ confusion.
the proceedings contain 17 papers. the special focus in this conference is on knowledge Discovery, knowledgeengineering and knowledge Management. the topics include: Automatic Categorization of software Reposito...
ISBN:
(纸本)9783031875687
the proceedings contain 17 papers. the special focus in this conference is on knowledge Discovery, knowledgeengineering and knowledge Management. the topics include: Automatic Categorization of software Repository Domains with Minimal Resources;personalized Book Recommendations for Adults Using Deep Learning and Sophisticated Filtering Approaches;dimensionality Reduction in Environmental Data;is there an Optimal Depth of Residual Networks?;modeling Interdependencies and Cascading Effects of Disasters on Critical Infrastructures;mechanical Fault Prediction based on Event knowledge Graph and Deep Learning;BioSTransformers for Health Ontologies Merging;methodology for Real Estate Recommendation based on Customer Behavior knowledge as Context-Approach;UpKG: A Framework for Integrating and Evaluating Novel Domains into knowledge Graphs;an Approach based on the Multifaceted Ontology to Development of Event Series Processing Tools;A Study of the Perceived Quality and Functionality of DEMO Process and Fact Models in the Health Domain and Improvements on Its Action Model;Revolutionary Synergy: the Fusion of Data Mesh and Data Fabric for Strategy Analytics in GRAPHYP knowledge Graph;an Innovative Framework for threshold Exceedance Forecasting in Timeseries Using Survival Analysis;promoting Sustained Use of Assistive Technology Among the Elderly: A User Experience and Causal Attribution theory Perspective on Caregiver Utterances;knowledge Management in Financial software Support Team: A Prototype based on Ontology.
Advancements in the internet as a technology over the years have come with a great challenge of malicious network attacks. the detection of such attacks which are known as network intrusions has become a very wel...
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We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying t...
ISBN:
(纸本)9798350323481
We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying the knowledge of the well-developed electronic design automation (EDA) field for SAT solving. Specifically, we first resort to logic synthesis algorithms to pre-process SAT instances into optimized and-inverter graphs (AIGs). By doing so, the distribution diversity among various SAT instances can be dramatically reduced, which facilitates improving the generalization capability of the learned model. Next, we regard the distribution of SAT solutions being a product of conditional Bernoulli distributions. based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling. To effectively train the generative model, withthe help of logic simulation tools, we obtain the probabilities of nodes in the AIG being logic '1' as rich supervision. We conduct comprehensive experiments on various SAT problems. Our results show that, DeepSAT achieves significant accuracy improvements over state-of-the-art learning-based SAT solutions, especially when generalized to SAT instances that are relatively large or with diverse distributions.
Cross-domain sequential recommendation (CDSR) is proposed to alleviate the data sparsity issue while capturing users’ sequential preferences. However, most existing methods do not explore the item transition patterns...
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Antimicrobial resistance is a global health challenge, complicating the treatment of bacterial infections and leading to higher patient morbidity and mortality. Rapid and reliable identification of resistant pathogens...
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Parkinson’s disease (PD) is a brain dysfunction condition that affects thousands of people across the globe. Early detection of Parkinson’s disease is crucial for effective treatment and control of symptoms. this st...
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this paper proposes a knowledge-based system for supporting the software specification process. It generates a plan for achieving the design goals, manages the tools according to the generated plan, and provides the n...
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ISBN:
(纸本)9789955258445
this paper proposes a knowledge-based system for supporting the software specification process. It generates a plan for achieving the design goals, manages the tools according to the generated plan, and provides the necessary suggestions and suitable graphical tools. the results of the experimental application of our system are also presented.
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