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Graph Contrast learning Fused Question Features Based Knowledge Tracing

Graph Contrast Learning Fused Question Features Based Knowle...

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international conference on Educational and Information Technology (ICEIT)

作者： Junyi Guo Ruixin Chen Zhun Zhang Qiongxiong Ma Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter School of Optoelectronic Science and Engineering South China Normal University Guangzhou China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

the exponential growth of online education has amplified the need for personalized learning, with Intelligent Tutoring Systems (ITS) providing rich behavioral interaction data for adaptive instruction. However, accurately assessing learners' knowledge states remains a significant challenge. Knowledge Tracing (KT), which predicts student performance based on historical interactions, has emerged as a promising solution. the complexity of KT lies in the multidimensional and dynamic nature of knowledge acquisition. In Graph-based Knowledge Tracing (GKT), current models face limitations in node importance representation, question embedding sparsity, and underutilization of learning features. To this end, this paper proposes the Graph Contrast learning Fused Question Features Based Knowledge Tracing (GCLFKT) model. Firstly, GCLFKT employs Graph Attention Networks (GAT) to generate aggregated question embeddings, mitigating data sparsity problems associated with question-level embeddings. Furthermore, it incorporates Graph Contrastive learning (GCL) to enhance embedding representations and integrates rich question features through a feature fusion layer. Experiments on the ASSISTments datasets demonstrate that GCLFKT outperforms several baseline models by over 4% in AUC metrics, with superior predictive performance validated through visualization analysis.

关键词： Measurement Analytical models Visualization Knowledge acquisition Education Contrastive learning Predictive models Complexity theory Information technology Faces

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Research and Implementation of Geography Curriculum Resource Construction Method Based on Knowledge Graph Technology

Research and Implementation of Geography Curriculum Resource...

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IEEE international Symposium on Information (IT) in Medicine and Education, ITME

作者： Hui Huang Xianmin Yang Hunan Automotive Engineering Vocational University Zhuzhou China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

the diversified and integrated learning needs make the construction of geography curriculum resources utilization become the focus of curriculum theory, which is the only way for the self-renewal and development of geography teaching. this paper intends to use literature research method, experiment method, combined with geography teaching theory, through the analysis of high school geography curriculum, combined with education knowledge map to generate high school geography knowledge relationship spectrum, and in-depth analysis of the advantages and disadvantages of the geography curriculum resource construction model based on knowledge graph technology. this research puts forward thinking for further developing geography curriculum resource construction in line with teaching objectives under the background of new curriculum reform, explores the effect of geography curriculum resource model based on knowledge graph technology in specific scenarios, optimizes the geography curriculum resource model, and provides more solutions and innovative ideas for geography curriculum resource construction.

关键词： Geography Analytical models Adaptive learning Education Knowledge graphs Big Data Data models Planning Information technology Medical diagnostic imaging

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Development and flow evaluation of a distance educational board game incorporating contextual learning mechanisms for children’s dental protection

Development and flow evaluation of a distance educational bo...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Ngu Pei-Ching Chien Chih-Chung Wang Min-Ching Ho Yen-Ting Yu Hai-An Hou Huei-Tse National Taiwan University of Science and Technology Taipei Taiwan Taipei Municipal Hospital WanFang Branch Taipei Taiwan

this study proposes a distance educational board game Fluoride Your Dental Health, for children to learn dental protection based on the theory of caries balance concept and game-based learning mechanisms. the game encourages children to think about problem-solving and adopt correct dental protection strategies. the game is a competitive game with role-playing, points, and rounds. through the game, learners can constantly explore and try to use the best strategies for dental care. this game was designed with the *** platform. We explored the flow and semi-structured questionnaires of 11 elementary school students on the game and found that the children’s overall performance in the flow of the game was high, suggesting that the game could help children increase their interest and participation in learning. However, the time children spent waiting for their turn might affect the flow in educational board game.

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Improving Students’ learning Effectiveness by an AIoT Human Centric Lighting System

Improving Students’ Learning Effectiveness by an AIoT Human...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Ya-Ting Yang Wei-Chun Chiang Jeen-Shing Wang Institute of Education National Cheng Kung University Tainan Taiwan Dept. of Electrical Engineering National Cheng Kung University Tainan Taiwan

this study aims to develop an AIoT human- centric lighting (HCL) system, encompassing its service platform and algorithms for analyzing and regulating circadian rhythms. the HCL system seamlessly integrates daylight-linked control into the IoT architecture, enabling remote control of luminaires, sensor data collection in classrooms, and the establishment of a cloud-based data-driven model. By leveraging optimal control theory, the system ensures consistent target settings for classroom illumination. the objective of this study is to investigate the effectiveness and long-term impact of the proposed lighting strategy, combined with acceptance and commitment therapy (ACT), on students’ sleep duration, depression levels, and learning effectiveness. Purposive sampling was conducted, involving four 10th-grade classes. the results of MANCOVA demonstrate that the posttests for sleep duration, depression, and academic achievement in the ACT class, the lighting strategy class (AIoT-based HCL system), and the combined lighting strategy with ACT class were significantly superior to those of the comparison class.

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Classification of Audio Streaming in Network Traffic Based on Machine learning Methods

Classification of Audio Streaming in Network Traffic Based o...

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international econference on Computer and Knowledge Engineering (ICCKE)

作者： Mohammad Nikbakht Mehdi Teimouri Information Theory and Coding (ITC) Laboratory University of Tehran Tehran Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

Classifying audio streaming in network traffic has become increasingly important due to its growing prevalence. Machine learning is commonly used for this classification, but generalizing these methods across different network environments remains challenging. this research evaluates and compares the generalization of two machine learning methods, “AppScanner” and “FlowPrint,” for classifying audio streaming applications in network traffic. We assess generalization through two phases: “validation” and “experiment.” In the validation phase, where the test data only slightly differed from the training data, FlowPrint outperformed AppScanner. In the experiment phase, where the test data varied significantly from the training data, both methods exhibited a loss in generalizability for detecting known classes, with performance drops of 96.6% for FlowPrint and 89.0% for AppScanner. Despite this, both methods showed strong generalizability in detecting unknown classes, with AppScanner and FlowPrint demonstrating improvements of 14.4% and 6.4%, respectively, in detecting unknown data.

关键词： Knowledge engineering Training data Telecommunication traffic Machine learning

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Research on Gamification of Diabetes Education

Research on Gamification of Diabetes Education

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IEEE international Symposium on Information (IT) in Medicine and Education, ITME

作者： Nengxian Luo Xiaoxia Meng Traditional Chinese Medicine Department Affiliated Hospital of Guizhou Medical University Guiyang China Guizhou Medical University School of Medicine and Health Management Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Diabetes education plays a significant role in medical education. Still, traditional teaching approaches have several drawbacks, including a reliance on a single approach and a gap between theory and practice. In order to improve student's learning experience and motivation, this paper provides an online gamified teaching solution for diabetes that makes use of gamification aspects and contemporary information technology. the program incorporates scenario simulation, character creation, a case system, and leaderboards to replicate a real clinic environment and to motivate students through certificates, points, and leaderboards. the program's implementation results demonstrated a substantial increase in the number of hours students spent online learning, their ability to comprehend knowledge points, and their test scores. Students generally found this learning method to be both engaging and effective, as indicated by questionnaire surveys. this study presents a novel teaching paradigm for diabetes education that is valuable for replication.

关键词： Surveys learning systems Education Diabetes Information technology

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uitPDFXAI: Malicious PDF files detection with eXplainable Artificial Intelligence

uitPDFXAI: Malicious PDF files detection with eXplainable Ar...

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IEEE international conference on System Engineering and Technology

作者： Nguyen Cam Hong Phuoc Vo Ngoc Tan Nguyen Tan Cam University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9798331504526

ISBN: (纸本)9798331504533

there are many security incidents related to PDF (Portable Document Format) files. Many studies have been proposed to detect malicious PDF files. In this study, we propose uitPDFXAI system to detect malicious PDF files by leveraging the powerful machine learning algorithms XGBoost and AdaBoost. Utilizing the SHapley Additive exPlanations, an explainable Artificial Intelligence, we provide a detailed explanation of the contribution of features to the model’s predictions, enhancing the transparency and interpretability. Our experiments are conducted on the CIC-Evasive-PDFMal2022 dataset. Experimental results show that the proposed system achieves the highest accuracy of 99.55% when using XGBoost. When using AdaBoost the system’s accuracy is 98.90%. the results of this study can be used for future research in the field of malicious PDF file detection.

关键词： Accuracy Machine learning algorithms Additives Predictive models Portable document format Systems engineering and theory Security Artificial intelligence

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Probabilistic (k, l)-Context-Sensitive Grammar Inference with Gibbs Sampling Applied to Chord Sequences 13

Probabilistic (<i>k</i>, <i>l</i>)-Context-Sensitive Grammar...

引用

13th international conference on Agents and Artificial Intelligence (ICAART)

作者： Lopes, Henrique Banos Freitas, Alan Univ Fed Ouro Preto Dept Comp Campus Univ Morro Cruzeiro Ouro Preto MG Brazil

ISBN: (纸本)9789897584848

Grammatical inference in computer music provides us with valuable models for fields such as algorithmic composition, style modeling, and music theory analysis. Grammars with higher accuracy can lead to models that improve the performance of various tasks in these fields. Recent studies show that Hidden Markov Models can outperform Markov Models in terms of accuracy, but there are no significant differences between Hidden Markov Models and Probabilistic Context-Free Grammars (PCFGs). this paper applies a Gibbs Sampling algorithm to infer Probabilistic (k, l)-Context-Sensitive Grammars (P(k, l)CSGs) and presents an application of P(k, l)CSGs to model the generation of chord sequences. Our results show Gibbs Sampling and P(k, l)CSGs can improve on PCFGs and the Metropolis-Hastings algorithm with perplexity values that are 48% lower on average (p-value 0:0026).

关键词： Grammatical Inference Computer Music Chord Sequence learning Probabilistic Context-Sensitive Grammars Machine learning algorithmic Composition Artificial Intelligence

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On the Contradictions in EFL Learners' Oral English learning from the Perspective of Activity theory

On the Contradictions in EFL Learners' Oral English Learning...

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2024 IEEE/WIC international conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2024

作者： Wang, Haoyun Ou, Chen School of International Studies NingboTech University Ningbo China

ISBN: (纸本)9798331504946

Oral language learning plays a significant role in English education. However, there are generally a series of contradictions in oral language learning today. Based on 'Activity theory', this study focused on 12 English major students to explore the emergence and resolution of contradictions. It utilized semi-structured interviews and case study methods typical of qualitative research and employed Nvivo12pro for text coding. the results show: (1) Students encounter four levels of contradictions in oral learning activities, with secondary contradictions being the most prominent. (2) When faced with these contradictions, students actively resolve them through self-reflection, self-adjustment, seeking help from others, or using mediating tools. (3) Addressing contradictions is a fundamental driving force for students' oral development. Contradictions can motivate students to engage in expansive learning, encouraging them to self-reflect and explore new methods of learning. © 2024 IEEE.

关键词： Students

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Enhancing Vehicle Aerodynamics with Deep Reinforcement learning in Voxelised Models

Enhancing Vehicle Aerodynamics with Deep Reinforcement Learn...

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Pattern Recognition Systems (ICPRS), international conference on

作者： Jignesh Patel Yannis Spyridis Vasileios Argyriou Department of Networks and Digital Media Kingston University London UK

ISBN: (数字)9798350375657

ISBN: (纸本)9798350375664

Aerodynamic design optimisation plays a crucial role in improving the performance and efficiency of automotive vehicles. this paper presents a novel approach for aerodynamic optimisation in car design using deep reinforcement learning (DRL). Traditional optimisation methods often face challenges in handling the complexity of the design space and capturing non-linear relationships between design parameters and aerodynamic performance metrics. this study addresses these challenges by employing DRL to learn optimal aerodynamic design strategies in a voxelised model representation. the proposed approach utilises voxelised models to discretise the vehicle geometry into a grid of voxels, allowing for a detailed representation of the aerodynamic flow field. the Proximal Policy Optimisation (PPO) algorithm is then employed to train a DRL agent to optimise the design parameters of the vehicle with respect to drag force, kinetic energy, and voxel collision count. Experimental results demonstrate the effectiveness and efficiency of the proposed approach in achieving significant results in aerodynamic performance. the findings highlight the potential of DRL techniques for addressing complex aerodynamic design optimisation problems in automotive engineering, with implications for improving vehicle performance, fuel efficiency, and environmental sustainability.

关键词： Drag Green products Force Optimization methods Aerodynamics Deep reinforcement learning Complexity theory

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