A5012930762- Online Learning and Analytics
- Intelligent Tutoring Systems and Adaptive Learning
- Topic Modeling
- Data Stream Mining Techniques
- Innovative Teaching and Learning Methods
- Context-Aware Activity Recognition Systems
- Machine Learning in Healthcare
- Domain Adaptation and Few-Shot Learning
- Immune Cell Function and Interaction
- Integrated Energy Systems Optimization
- Machine Learning and ELM
- Advanced Graph Neural Networks
- Data Visualization and Analytics
- Multimodal Machine Learning Applications
- Energy Load and Power Forecasting
- Tactile and Sensory Interactions
- Electric Power System Optimization
- Machine Learning and Data Classification
- Multimedia Communication and Technology
- T-cell and B-cell Immunology
- Caching and Content Delivery
- Video Surveillance and Tracking Methods
- AI-based Problem Solving and Planning
- IL-33, ST2, and ILC Pathways
- Recommender Systems and Techniques
Beijing University of Posts and Telecommunications
2021-2024
Kyoto University
2022
University of Science and Technology Beijing
2022
Baotou Teachers College
2022
Ministry of Housing and Urban-Rural Development
2017
Student dropout prediction (SDP) in educational research has gained prominence for its role analyzing student learning behaviors through time series models. Traditional methods often focus singularly on either accuracy or earliness, leading to sub-optimal interventions at-risk students. This issue underlines the necessity that effectively manage trade-off between and earliness. Recognizing limitations of existing methods, this study introduces a novel approach leveraging multi-objective...
Invariant natural killer T (iNKT) cells are a group of innate-like lymphocytes that recognize lipid antigens. They supposed to be tissue resident and important for systemic local immune regulation. To investigate the heterogeneity iNKT cells, we recharacterized in thymus peripheral tissues. were divided into three subpopulations by expression cell receptor CD244 chemokine CXCR6 designated as C0 (CD244-CXCR6-), C1 (CD244-CXCR6+), or C2 (CD244+CXCR6+) cells. The development maturation from...
With the increasing demands of personalized learning, knowledge tracing has become important which traces students' states based on their historical practices. Factor analysis methods mainly use two kinds factors are separately related to students and questions model states. These total number attempts learning progress hardly highlight impact most recent relevant Besides, current factor ignore rich information contained in questions. In this paper, we propose Multi-Factors Aware...
As the core of Knowledge Tracking (KT) task, assessing students' dynamic mastery knowledge concepts is crucial for both offline teaching and online educational applications. Since often unlabeled, existing KT methods rely on implicit paradigm historical practice to responses practices address challenge unlabeled concept mastery. However, purely predicting student without imposing specific constraints hidden values does not guarantee accuracy these intermediate as values. To this issue, we...
Student Dropout Prediction (SDP) is pivotal in mitigating withdrawals Massive Open Online Courses. Previous studies generally modeled the SDP problem as a binary classification task, providing single prediction outcome. Accordingly, some attempts introduce survival analysis methods to achieve continuous and consistent predictions over time. However, volatility sparsity of data always weaken models' performance. Prevailing solutions rely heavily on pre-processing independent predictive...
Visually impaired people face many inconveniences in daily life, and there are problems such as high prices single functions the market of assistance tools for visually people. In this work, we designed implemented a low-cost intelligent cane, particularly individuals, based on computer vision, sensors, an edge-cloud collaboration scheme. Obstacle detection, fall traffic light detection have been integrated convenience moving We also image captioning function object with high-speed...
Developing low carbon electricity is a powerful measure to deal with global climate problems, which also the internal motive force promote sustainable development of economy and society. As clean energy, photovoltaic power generation has developed rapidly in recent years, great impact on energy saving emission reduction system. In this paper, an economic dispatch model considering CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>...
Existing methods often adjust representations adaptively only after aggregating user behavior sequences. This coarse-grained approach to re-weighting the entire sequence hampers model's ability accurately model interest migration across different scenarios. To enhance capacity capture interests from historical sequences in each scenario, we develop a ranking framework named Scenario-Adaptive Fine-Grained Personalization Network (SFPNet), which designs kind of fine-grained method for...
With the increasing demands of personalized learning, knowledge tracing has become important which traces students' states based on their historical practices. Factor analysis methods mainly use two kinds factors are separately related to students and questions model states. These total number attempts learning progress hardly highlight impact most recent relevant Besides, current factor ignore rich information contained in questions. In this paper, we propose Multi-Factors Aware...
Knowledge tracing (KT) aims to predict students' responses practices based on their historical question-answering behaviors. However, most current KT methods focus improving overall AUC, leaving ample room for optimization in modeling sequences of excessive or insufficient lengths. As get longer, computational costs will increase exponentially. Therefore, usually truncate an acceptable length, which makes it difficult models online service systems capture complete practice behaviors students...
The Knowledge Tracing (KT) task plays a crucial role in personalized learning, and its purpose is to predict student responses based on their historical practice behavior sequence. However, the KT suffers from data sparsity, which makes it challenging learn robust representations for students with few records increases risk of model overfitting. Therefore, this paper, we propose Cognition-Mode Aware Variational Representation Learning Framework (CMVF) that can be directly applied existing...
The Knowledge Tracing (KT) task plays a crucial role in personalized learning, and its purpose is to predict student responses based on their historical practice behavior sequence. However, the KT suffers from data sparsity, which makes it challenging learn robust representations for students with few records increases risk of model overfitting. Therefore, this paper, we propose Cognition-Mode Aware Variational Representation Learning Framework (CMVF) that can be directly applied existing...
Student Dropout Prediction (SDP) is of pivotal significance in mitigating withdrawals Massive Open Online Courses. Research these areas are usually carried out using deep learning to detect complex nonlinear patterns students' sequences. However, the volatility and sparsity data always weaken performance neural networks. Prevailing approaches required an additional smoothing or interpolation step independent prediction model, which may lose valuable information introduce inauthentic data....