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Young Children with Autism Spectrum Disorder learning Emotion-Recognition from Animated Media Content 14

Young Children with Autism Spectrum Disorder Learning Emotio...

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14th international Multi-conference on Complexity, Informatics and Cybernetics, IMCIC 2023

作者： Ryan, Erin School of Communication and Media Kennesaw State University KennesawGA30144 United States

ISBN: (纸本)9781950492718

Research suggests that children with Autism Spectrum Disorder (ASD) can learn executive functioning (EF) skills like emotion-recognition from media content such as thomas the Tank Engine. Whereas those researchers posited the reason for such learning lies in idiosyncrasies related to ASD itself, this author argues the way media is crafted is the key element in determining successful learning. An interdisciplinary approach is needed to uncover how children with ASD can best learn from mediated content, especially regarding EF skills. By bridging Autism research with communication research, best practices for creating media content to teach EF skills to young children with ASD can be cultivated. A hybrid theory of cognitive capacity and multimedia learning by high-systemizers, in which elements from four theories focusing on working memory and the limitations of the cognitions of the viewers (Cognitive Load theory, Limited Capacity Model of Motivated Mediated Message Processing, the Capacity Model, and the Cognitive theory of Multimedia learning) were combined, indicates that content creators must keep things simple in order to make a mediated character accessible to children with ASD. A small survey of parents of children with ASD bears this out;parents were asked about their child's relationship with media, what has been successful in their home, whether there are favorite programs/characters, and whether they believe their children are learning EF skills from media. Results indicated parents believe their children have special relationships with mediated characters they cannot duplicate with people. Some parents noted their children displaying better emotion-recognition after viewing. the formal characteristics of favorite programs indicate support for the hybrid theory of cognitive capacity and multimedia learning by high-systemizers. © 2023 Proceedings IMCIC - international Multi-conference on Complexity, Informatics and Cybernetics.

关键词： Emotion Recognition

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Predicting the minimum energy pathway of 1H to 1T phase transition of select 2D transition metal dichalcogenides via density functional theory and machine learning approach 14

Predicting the minimum energy pathway of 1H to 1T phase tran...

引用

2024 14th international conference on Applied Physics and Mathematics, ICAPM 2024

作者： Diego, K.Y.M. Putungan, D.B. Santos-Putungan, A.B. Institute of Mathematical Sciences and Physics University of the Philippines Los Baños Philippines

this study predicts the minimum energy pathways (MEPs) and transition barriers of two-dimensional transition metal dichalcogenides (TMDs) during their 1H to 1T structural phase transitions. the investigation utilizes density functional theory (DFT) calculations and machine learning algorithms to predict the MEPs and transition barriers. Six TMDs, namely NbSe2, ScS2, ScSe2, TiTe2, VS2, and VSe2, are selected for analysis. the DFT calculations provide reference values for comparison with the machine learning predictions. the transition barriers obtained through DFT calculations range from 0.13 eV to 0.72 eV. the machine learning models, including Linear Regression, Random Forest Regression, K-Nearest Neighbor Regression, and Gaussian Process Regression, demonstrate varying degrees of accuracy in predicting the transition barriers. Gaussian Process Regression achieved the highest accuracy among all ML models, with the absolute mean error spanning all systems being 0.0093. It has been shown that the features that were identified in the study are sufficient for the prediction of the MEPs and transition barriers of TMDs using machine learning. Overall, this study contributes to the understanding of TMD phase transitions and provides insights into the integration of machine learning in materials science research. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Transition metals

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Policy-Based Reinforcement learning for Assortative Matching in Human Behavior Modeling 14th

Policy-Based Reinforcement Learning for Assortative Matching...

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14th international conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management (DHM) Held as Part of the 25th international conference on Human-Computer Interaction (HCII)

作者： Deng, Ou Jin, Qun Waseda Univ Grad Sch Human Sci Tokorozawa Japan

ISBN: (纸本)9783031357473;9783031357480

this paper explores human behavior in virtual networked communities, specifically individuals or groups' potential and expressive capacity to respond to internal and external stimuli, with assortative matching as a typical example. A modeling approach based on Multi-Agent Reinforcement learning (MARL) is proposed, adding a multi-head attention function to the A3C algorithm to enhance learning effectiveness. this approach simulates human behavior in certain scenarios through various environmental parameter settings and agent action strategies. In our experiment, reinforcement learning is employed to serve specific agents that learn from environment status and competitor behaviors, optimizing strategies to achieve better results. the simulation includes individual and group levels, displaying possible paths to forming competitive advantages. this modeling approach provides a means for further analysis of the evolutionary dynamics of human behavior, communities, and organizations in various socioeconomic issues.

关键词： Multiagent system Reinforcement learning Game theory Human behavior modeling

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JSAI Annual conference, JSAI 2022, and the 14th JSAI international Symposia on Artificial Intelligence, JSAI-isAI 2022

JSAI Annual Conference, JSAI 2022, and the 14th JSAI Interna...

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JSAI Annual conference, JSAI 2022, and the 14th JSAI international Symposia on Artificial Intelligence, JSAI-isAI 2022

ISBN: (纸本)9783031291678

the proceedings contain 18 papers. the special focus in this conference is on Artificial Intelligence. the topics include: Less is Better: Constructing Legal Question Answering System by Weighing Longest Common Subsequence of Disjunctive Union Text;proposal for Turning Point Detection Method Using Financial Text and Transformer;Product Portfolio Optimization for LTV Maximization;An Examination of Eating Experiences in Relation to Psychological States, Loneliness, and Depression Using BERT;objective Detection of High-Risk Tackle in Rugby by Combination of Pose Estimation and Machine learning;incremental Informational Value of Floorplans for Rent Price Prediction: Applications of Modern Computer Vision Techniques in Real-Estate;transaction Prediction by Using Graph Neural Network and Textual Industry Information;overfitting Problem in the Approximate Bayesian Computation Method Based on Maxima Weighted Isolation Kernel;on Complexity and Generality of Contrary Prioritized Defeasible theory;mapping Similar Provisions Between Japanese and Foreign Laws;COLIEE 2022 Summary: Methods for Legal Document Retrieval and Entailment;JNLP Team: Deep learning Approaches for Tackling Long and Ambiguous Legal Documents in COLIEE 2022;semantic-Based Classification of Relevant Case Law;nigam@COLIEE-22: Legal Case Retrieval and Entailment Using Cascading of Lexical and Semantic-Based Models;HUKB at the COLIEE 2022 Statute Law Task;using Textbook Knowledge for Statute Retrieval and Entailment Classification.

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Appliance-Incremental learning for Non-Intrusive Load Monitoring 14

Appliance-Incremental Learning for Non-Intrusive Load Monito...

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14th IEEE international conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Tanoni, Giulia Principi, Emanuele Mandoliniy, Luigi Squartini, Stefano Università Politecnica Delle Marche Department of Information Engineering Ancona Italy Mac Srl MC Recanati Italy

ISBN: (纸本)9781665455541

Non-Intrusive Load Monitoring (NILM) is an algorithmic-based approach for monitoring appliances inside a building using a single metering point, enabling better traceability of energy use within the grid. Most state-of-the-art approaches, that are based on Deep Neural Networks (DNN), are 'static' and present serious limitations in handling changes in appliance usage patterns or the addition of new appliances. this limits the adaptability of NILM in practical scenarios. To address this, we propose an Appliance-Incremental learning (AIL) method that can continuously adapt a DNN-based NILM algorithm to monitor new appliances without introducing new networks. AIL utilizes a multi-label classification framework, sharing the same network structure for all appliances. We mitigate catastrophic forgetting by distilling knowledge of previous tasks and dynamically selecting network parameters better related to new tasks. this allows us to efficiently train the network on new tasks while maintaining stable performance on previous appliances. We compare our method with the static NILM approach and the learning without Forgetting (LwF) method in a real-world scenario, where a pre-trained model is adapted to new environments post-deployment. the results demonstrate the effectiveness of our method in handling new appliances, achieving improved adaptability and performance with respect to comparative methods. the proposed AIL method offers a reliable solution for NILM, enabling the monitoring of new appliances and accommodating variations in usage patterns. © 2023 IEEE.

关键词： Deep neural networks

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Prediction and Optimization of Mechanical Manufacturing Quality Based on Deep learning 7th

Prediction and Optimization of Mechanical Manufacturing Qual...

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7th international conference on Innovative Computing, IC 2024

作者： Han, Dong Dong, Tao Zhu, Songyu Industrial Center Harbin Vocational and Technical College Harbin150000 China Research Office Harbin Vocational and Technical College Harbin150000 China School of Electronics and Information Engineering Harbin Vocational and Technical College Harbin150000 China

ISBN: (纸本)9789819741922

the application of deep learning technology in mechanical design parameter optimization has opened up new research directions. the limitations of traditional design methods, especially in the search for global optimal solutions, have prompted researchers to explore more efficient solutions. Deep learning, with its outstanding performance in pattern recognition and predictive analysis, is considered a powerful tool for mechanical design optimization. through comparative analysis and case studies, the accuracy of deep learning models in predicting mechanical manufacturing quality and their potential application in design parameter optimization were demonstrated. through experiments, it was found that the deep learning technology quality prediction system predicted 187 perfect products, 145 medium products, and 147 defective products for mechanical quality, with a prediction error of 21, and a high overall judgment accuracy rate. It can be seen that deep learning methods can significantly improve design efficiency, reduce costs, and improve the overall performance of mechanical systems. this study aimed to provide an efficient optimization strategy for the field of mechanical design to promote the improvement of performance and reliability. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pattern recognition

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14th international Symposium on Continuum Models and Discrete Systems, CMDS 2023

14th International Symposium on Continuum Models and Discret...

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14th international Symposium on Continuum Models and Discrete Systems, CMDS 2023

ISBN: (纸本)9783031586644

the proceedings contain 21 papers. the special focus in this conference is on Continuum Models and Discrete Systems. the topics include: Continuous Simulation of Heterogeneous Media: the Karhunen-Loève Approach Versus the Turning Bands Method;morphological Models of Networks of Inter-granules Micropores: Identification and Validation Based on 2D and 3D Imaging;collective Motion of Epithelial Cells in Tissues and the Active Vertex Model;structure of Fields in Extremal 2D Conducting Multimaterial Composites;effect of Crystallographic Twins on the Elastoplastic Response of Polycrystals;continuum Modelling of Orthotropic Hexatruss Lattice Materials: Effective Stiffness and Experimental Validation;experimental Characterization and Numerical Modeling of the Porosity Formation Mechanisms in thermoplastic Laminates at High Temperature;An Adaptive Version of the Eyre-Milton Solution Scheme for FFT-Based Homogenization of Composites;a Stabilised Micropolar theory Derived from a Periodic Beam Lattice;interaction Between Gravity and Gyroscopic Forces: Transient Control of Gyropendulums and Inverted Gyropendulums;manipulation of the Optical Properties of a Plasmonic Metamaterial Film by an External Transverse Voltage;variational Principle for a Damped, Quadratically Interacting Particle Chain with Nonconservative Forcing;optimal Design of Programmable Mechanical Metamaterials;modeling Multi-grain Multi-hole thin Solid State Films;choquet Capacity Networks for Random Point Process Classification and Regression;simulation of Microstructures and Machine learning;fuzzy Inference System for Fatigue Parameters Prediction in Metals: from Strength to Fatigue;using Neural Networks to Explore Structure-Property Relations in Bio-Inspired Impact-Resistant Structures.

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On Computing Homological Hitting Sets 14

On Computing Homological Hitting Sets

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14th Innovations in theoretical Computer Science conference, ITCS 2023

作者： Bauer, Ulrich Rathod, Abhishek Zehavi, Meirav Department of Mathematics TUM School CIT Technische Universität München Germany Department of Computer Science Purdue University West LafayetteIN United States Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9783959772631

Cut problems form one of the most fundamental classes of problems in algorithmic graph theory. In this paper, we initiate the algorithmic study of a high-dimensional cut problem. the problem we study, namely, Homological Hitting Set (HHS), is defined as follows: Given a nontrivial r-cycle z in a simplicial complex, find a set S of r-dimensional simplices of minimum cardinality so that S meets every cycle homologous to z. Our first result is that HHS admits a polynomial-time solution on triangulations of closed surfaces. Interestingly, the minimal solution is given in terms of the cocycles of the surface. Next, we provide an example of a 2-complex for which the (unique) minimal hitting set is not a cocycle. Furthermore, for general complexes, we show that HHS is W[1]-hard with respect to the solution size p. In contrast, on the positive side, we show that HHS admits an FPT algorithm with respect to p + ∆, where ∆ is the maximum degree of the Hasse graph of the complex K. © Ulrich Bauer, Abhishek Rathod, and Meirav Zehavi;licensed under Creative Commons License CC-BY 4.0.

关键词： Graph theory

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Mechanism and Path of Integrated Development of Industrial Internet and Manufacturing from the Perspective of Endogenous Growth theory 23

Mechanism and Path of Integrated Development of Industrial I...

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14th international conference on E-Education, E-Business, E-Management and E-learning, IC4E 2023

作者： Jianping, Dai School of Digital Economics Changzhou College of Information Technology China

ISBN: (纸本)9798400700651

this paper deeply analyzes the theoretical mechanism and path of the integrated development of industrial Internet and manufacturing by using the endogenous growth model based on the perspective of endogenous growth theory. It is found that knowledge and talents play a decisive role in the high-quality development of the manufacturing. the industrial Internet has positive externalities on the growth of the manufacturing, and can promote the upgrading of products and services in the manufacturing. Direct investment in the industrial Internet can break through the general accumulation effect and promote the industrial agglomeration of manufacturing. the industrial Internet enables the development of manufacturing by means of quality, efficiency and industrial structure reform, and promotes the deep integration with manufacturing. © 2023 ACM.

关键词： Economics

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Research on Pattern Recognition of Urban and Rural Consumption Behavior Based on Machine learning and Its Application in Digital Economy 2

Research on Pattern Recognition of Urban and Rural Consumpti...

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2nd international conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Mu, Xiaowei Liu, Yan Sheng, Zhijun Chen, Jiahui Changchun Normal University Changchun Humanities and Sciences College Changchun China Changchun Humanities and Sciences College Changchun China Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798350386639

this paper introduces an SVM algorithm for studying consumer behavior. this model is a new pattern recognition algorithm based on statistical principle. It has unique advantages in dealing with small samples, nonlinearity and high dimensional pattern recognition. the fuzzy semantic modeling method is constructed by using emotion computing and text mining. At the same time, the internal and external inducement factors are the first part and the purchasing degree of consumers is the second part, and the fuzzy reasoning model is established. the consumption behavior and process of urban and rural residents are constructed by using probability theory. through the analysis of the relationship between the elements, the corresponding knowledge base is established. the unreasonable purchase degree model of customers is established by Jess method. Finally, taking a tobacco enterprise as an example, an empirical study was made on it. the results show that in urban and rural areas, SVM-based consumer behavior identification and statistical analysis has high accuracy and efficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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