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International Conference on Advanced Learning Technologies (ICALT)

作者： Chia-Kai Chang Po-Chung Chen Zih-Syun Chen Tonny Menglun Kuo Center for General Education National Central University Taoyuan City Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City Taiwan Department of Civil Engineering National Central University Taoyuan City Taiwan Center for Teaching and Learning Development National Tsing Hua University Hsinchu City Taiwan

Interdisciplinary knowledge sharing is a crucial component of higher education. The use of asynchronous online discussion forums as a medium for fostering interdisciplinary knowledge sharing is effective, as it allows for the sharing, posting, and reflecting of information among learners outside of traditional classroom settings. However, the sheer volume of posts in such forums can threaten the quality of discussion and the ability of instructors to provide timely evaluations. To address this issue, an automated post-rating system has been developed utilizing a BERT-based AI model. This system evaluates learners' posts and provides prompt categorization outcomes into three categories: non-informative, informative, and neutral, within 10 seconds. Our model demonstrated appropriate accuracy in assessing the information density in forum posts, indicating potential benefits for both learners and instructors. Specifically, it achieved 67%, 68%, and 75% accuracy rates for posts categorized as discussion, comment, and reply, respectively. To assess the effectiveness of the system, it was tested and evaluated using two courses, “Python Programming” and “Introduction to AI,” through the use of a questionnaire. Results revealed that learners held positive evaluations of the system, noting improvements in post quality, reduced plagiarism, and enhanced comprehension. Additionally, feedback from open-ended questions also indicated the benefits of automatic feedback on post quality.

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When to Update Your Model: Constrained Model-based Reinforcement Learning

arXiv

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arXiv 2022年

作者： Ji, Tianying Luo, Yu Sun, Fuchun Jing, Mingxuan He, Fengxiang Huang, Wenbing Department of Computer Science and Technology Tsinghua University China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences China JD Explore Academy *** Inc China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization and model learning. Existing discrepancy bounds generally ignore the impacts of model shifts, and their corresponding algorithms are prone to degrade performance by drastic model updating. In this work, we first propose a novel and general theoretical scheme for a non-decreasing performance guarantee of MBRL. Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. These discoveries encourage us to formulate a constrained lower-bound optimization problem to permit the monotonicity of MBRL. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns. Motivated by these analyses, we design a simple but effective algorithm CMLO1 (Constrained Model-shift Lower-bound Optimization), by introducing an event-triggered mechanism that flexibly determines when to update the model. Experiments show that CMLO surpasses other state-of-the-art methods and produces a boost when various policy optimization methods are employed. © 2022, CC BY-NC-SA.

关键词： Constrained optimization

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Accurate Classroom Person Detection Based on Multi-Frame Feature Fusion with Attention

Accurate Classroom Person Detection Based on Multi-Frame Fea...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Guoan Cheng Jiandong Ren Yuping Li Shengke Wang Yougang Ding Hao Wang School of information & Electrical Engineering Qingdao Harbour Vocational & Technical College Qingdao China Student Work Department Qingdao Harbour Vocational & Technical College Qingdao China College of Computer Science and Engineering Ocean University of China Qingdao China Mathematics Teaching and Research Group Experimental School Affiliated to Shandong University Heze China

In recent years, object detection technology has developed rapidly and is widely used in the field of intelligent video monitoring, especially in the classroom scenes, where incorporating the intelligent object detection technique into the video monitoring systems for intelligent monitoring purpose has important practical significance. However, surveillance videos may encounter issues such as object occlusion, motion blur, posture changes, and video defocus, which poses great challenges to object detection in the surveillance video scenarios. Therefore, in this paper, we conduct in-depth research on human detection in classroom multi camera surveillance video scenes and propose a multi frame feature fusion network based on the attention mechanism. The algorithm first adds the dilated convolution layers to the ResNet50 backbone network to expand the receptive field and retain more feature information; Secondly, a multi frame feature fusion strategy based on the attention mechanism was proposed to enhance the feature representation ability of the deep detector by fusing the feature information of adjacent frames, thereby improving the accuracy of human detection in the classroom monitoring video. To verify the effectiveness of the proposed method, we have compiled and produced a large-scale classroom monitoring video dataset, named OUC-ClassVideo. The proposed object detection network, which is developed based on the attention mechanism and multi frame feature fusion strategy, has achieved satisfactory performance on both the public dataset ImageNet VID and the OUC-ClassVideo dataset collected by ourselves.

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Application of Machine Learning Techniques to the Prediction of Student Success

Application of Machine Learning Techniques to the Prediction...

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IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE International

作者： Eluwumi Buraimoh Ritesh Ajoodha Kershree Padayachee School of Computer Science and Applied Mathematics The University of the Witwatersrand Johannesburg South Africa Centre for Learning Teaching and Development The University of the Witwatersrand Johannesburg South Africa

This study presents six machine learning models in the prediction of student success in a technology-mediated environment. Student behavioral attributes with a learning management environment have proven to be a significant determinant in forecasting students' performance. This study attempts to provide the model with optimum accuracy to determine students who need assistance to improve their educational performances and other learning outcomes. We examined the impacts of SMOTE data re-sampling and the effect of attribute selection in this study. The models' performances were enhanced with the resampling method as the imbalanced dataset was identified to have performed poorly. Attribute Selection with the top ten attributes and 10-fold cross-validation offer best performances. The six predictive models utilized in this study are Linear Discriminant Analysis, Logistic Regression, Classification and Regression Tree, K-Nearest Neighbour, Naïve Bayes Classifier, and Support Vector Machines. Classification and Regression Tree model and Linear Regression had the best accuracy score of 0.86 after 10-fold cross-validation and top ten attribute selection. This study concludes that student behavioral attributes are useful predictors of student success.

关键词： Support vector machines Training Mechatronics Linear regression Machine learning Predictive models Data models

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Study for the improvement of the moroccan public higher education system based on a strategic plan for learning technologies 22

Study for the improvement of the moroccan public higher educ...

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22nd International Symposium on computers in Education, SIIE 2020

作者： García-Peñalvo, Francisco José Sánchez-Prieto, José Carlos Rodríguez-Conde, M. José Gamazo, Adriana García-Holgado, Alicia Abad, Fernando Martínez Grial Research Group Iuce Computer Science Department University of Salamanca Salamanca Spain Grial Research Group Iuce Didactics Organization and Research Methods Department University of Salamanca Salamanca Spain

Within the framework of the comprehensive educational reform of Moroccan universities, the twinning project financed by the European Union has the general objective of accompanying, assisting and promoting the reform of the Moroccan higher education system within the framework of its strategic vision 2015-2030 and its approach to the European Higher Education Area, which has a twofold aim: To enhance the employability of graduates and to improve the governance of the university system. The purpose of this paper is to present the work carried out in the component 3 of this project, focused on the diversification of teaching methods (elearning strategy). This component has aimed to provide guidelines and recommendations for addressing the main problems of Higher Education in Morocco and improving its quality. © 2020 CEUR-WS. All rights reserved.

关键词： E-learning

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Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

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"Prompt-Gamma Neutron Activation Analysis (PGNAA)" Metal Spectral Classification using Deep Learning Method

arXiv

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arXiv 2022年

作者： Cheng, Ka Yung Shayan, Helmand Krycki, Kai Lange-Hegermann, Markus Campus Kiel Kaistraße 101 Kiel24114 Germany Department of Electrical Engineering & Computer Science OWL University of Applied Sciences and Arts Campusallee 12 Lemgo32657 Germany Department of Mathematical Methods & Software Development AiNT GmbH Stolberg52222 Germany

There is a pressing market demand to minimize the test time of Prompt Gamma Neutron Activation Analysis (PGNAA) spectra measurement machine, so that it could function as an instant material analyzer [1]–[3], e.g. to classify waste samples instantaneously and determine the best recycling method based on the detected compositions of the testing sample. This article introduces a new development of the deep learning classification and contrive to reduce the test time for PGNAA machine. We propose both Random Sampling methods and Class Activation Map (CAM) to generate "downsized" samples and train the CNN model continuously. Random Sampling methods (RSM) aims to reduce the measuring time within a sample, and Class Activation Map (CAM) is for filtering out the less important energy range of the downsized samples. We shorten the overall PGNAA measuring time down to 2.5 seconds while ensuring the accuracy is around 96.88% for our dataset with 12 different species of substances. Compared with classifying different species of materials, it requires more test time (sample count rate) for substances having the same elements to archive good accuracy. For example, the classification of copper alloys requires nearly 24 seconds test time to reach 98% *** Codes 68T07, 82D35 Copyright © 2022, The Authors. All rights reserved.

关键词： Chemical activation

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Singular value decomposition-based virtual representation for face recognition

Singular value decomposition-based virtual representation fo...

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作者： Liu, Shigang Wang, Yuhong Peng, Yali Hou, Sujuan Zhang, Keyou Wu, Xiaojun Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China School of Computer Science Shaanxi Normal University Xi’an710119 China School of Information Science and Engineering Shandong Normal University Jinan250014 China

In the field of face recognition, a key issue is whether there are a sufficient number of face training samples with valid information. Due to the complexity of human face images, face recognition is easy to be affected by the external environment such as light intensity, gesture expression, hairstyle, and occlusion. Therefore, it is difficult to obtain enough effective samples in practical applications. In this paper, we propose a new algorithm that generates virtual images by utilizing the information of the test sample via singular value decomposition. The virtual images not only extend the training sample set but also can better adapt to the test sample. In addition, we use the weighted score fusion scheme to calculate the ultimate result, which can better take advantages of data from different sources including original images and virtual images. Experimental results on the Extended Yale_B, AR, GT, ORL, and FERET face databases prove that our algorithm can obtain satisfactory performance. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Singular value decomposition

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A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot 6

A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

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6th International Conference on Robotics and Automation Engineering, ICRAE 2021

作者： Chengbo, Li Xiaoming, Wang Zixi, Huang Guangyao, Pang Xinyu, Han Yaguang, Lin Dalian Free Trade Zone No. 1 High School Dalian China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Shaanxi Normal University Xi'an710119 China Shenzhen Middle School Shenzhen China Wuzhou University School of Data Science and Software Engineering Wuzhou China Singapore Polytechnic Advanced Robotics and Intelligent Control Centre Singapore

ISBN: (纸本)9781665406970

The application of robots to assist humans in earthquake rescue has always been an important topic that researchers are constantly studying. CoSpace Rescue is an abstract modeling and simulation of earthquake rescue and a simulation platform for studying robot rescue search algorithms. Its mathematical model is a multi-objective optimization problem. In this paper, we propose a search algorithm design framework for the CoSpace Rescue robot. Based On this framework and the idea of the heuristic search algorithm, we design and implement the 'Multi-objective Optimal Dynamic Adaptive On-Demand Search Algorithm'. The results of the experiment show that the algorithm can dynamically adapt to the changes in the environment and significantly improve the search efficiency of the rescue robot. © 2021 IEEE.

关键词： Earthquakes

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Improving the Encapsulation Efficiency of Lipase in Molecular Cages and Its Application

SSRN

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SSRN 2022年

作者： Xiao, Yun-Yan Zhang, Jiang-Tao Dong, Yong-Hua Li, Shu-Li Zhi, Gao-Ying Zhang, Ya-Fang Zhang, Dong-Hao College of Pharmaceutical Science Hebei University Baoding071002 China Key Laboratory of Pharmaceutical Quality Control of Hebei Province College of Pharmaceutical Science Hebei University Baoding071002 China Institute of Life Science and Green Development Hebei University Baoding071002 China Department of Computer Teaching Hebei University Baoding071002 China Pharmacy Department Baoding Children’s Hospital China

Here, lipase encapsulation is constructed by locking enzyme molecules in nanomolecular cages on the surface of SH-PEI@PVAC magnetic microspheres. The encapsulation efficiency is improved obviously by efficiently modifying thiol groups using 3-mercaptopropionic acid. N2 adsorption-desorption isotherms reveal the existence of mesoporous molecular cages on microsphere surface. The robust immobilizing strength of carrier to lipase demonstrates the successful encapsulation of enzyme in nanomolecular cages. The encapsulated lipase shows high enzyme loading (52.9 mg/g) and high activity (51.4 U/mg). Different sizes of molecular cages are established and cage size showed important effects on lipase encapsulation. It shows that enzyme loading is low at small size of molecular cages, which is attributed to that the nanomolecular cage is too small to house lipase. The investigation in lipase conformation suggests that the encapsulated lipase retains its active conformation. Compared with the adsorbed lipase, the encapsulated lipase shows higher thermal stability (4.9 times) and higher resistance to denaturant (5.0 times). Encouragingly, the encapsulated lipase shows high activity and reusability in lipase-catalyzed synthesis of propyl laurate, suggesting the potential application value of encapsulated lipase. © 2022, The Authors. All rights reserved.

关键词： Enzyme immobilization

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