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computer-Assisted Knowledge Points and Question Difficulty Classification Based on BERT

Computer-Assisted Knowledge Points and Question Difficulty C...

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2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Zhu, Jia Jun Yang, Zichuan Hong, Binjie Song, Jiacheng Wang, Jiwei Chen, Tianhao Yang, Shuilan Lan, Zixun Ma, Fei Department of Information and Computing Science Xi'an Jiaotong-Liverpool University Suzhou China School of Engineering Suzhou Centennial College Suzhou China Department of Applied Mathematics Xi'an Jiaotong-Liverpool University Suzhou China GROW Inc. Suzhou China EasyStack Inc. Nanjing China TAL Education Group Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

In recent years, with the increasing use of educational technology and online learning platforms, there has been a growing interest in developing intelligent systems that can automatically predict the knowledge points associated with educational questions. This paper presents a novel approach for knowledge point prediction in middle school mathematics questions. The dataset used in this study consists of a large collection of 591,379 middle school mathematics questions. To leverage the power of natural language processing techniques, the questions are preprocessed using a tokenizer and encoded into word embeddings using BERT (Bidirectional Encoder Representations from Transformers). Matrix dot production is then employed to calculate the similarity between each test question and the training set, and the top-n most similar vectors are selected for each test question. A voting mechanism is introduced to eliminate duplicate knowledge points and rank them based on their total scores in descending order, resulting in k candidate solutions. The performance of the proposed approach is evaluated using top-k accuracy as the evaluation metric and a lowest common ancestor (LCA) algorithm is used to calculate accuracy at different levels of the knowledge point tree. The results demonstrate the effectiveness of the proposed approach in predicting knowledge points for middle school mathematics questions and its potential for application in intelligent tutoring systems and educational assessment tools. © 2025 SPIE.

关键词： Natural language processing systems

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Improve the Semantic Perception Ability of CNN-Based Object Detection Systems with Attention Model

Improve the Semantic Perception Ability of CNN-Based Object ...

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2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Shi, TianYang Xu, XinYin Fu, Wei Huang University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Convolutional neural networks(CNNs) based object detection methods are prone to be interfered with by background noise and cannot make full use of semantic information in the positive sample-choosing phase. To overcome these issues, first, we proposed an external module called "Semantic Information Attention Module (SIAM)". The SIAM module can reduce the influence of features from non-target areas. Furthermore, we proposed a new positive sample selection strategy "Heat IOU", and the "Heat IOU" standard will give candidate samples with more semantic information more weight in the positive sample selection phase. Note that SIAM and Heat IOU can be easily combined with existing convolutional backbones, which provide a flexible plug-and-play way to spur the object detection system to focus on the zones in the image with abundant semantic information in forward propagation and positive sample choosing phase. Experimental results demonstrate that our SIAM and Hot IOU successfully improve the performance of the object detection task. The code is available at: https://***/OZERO98/SIAM. © 2025 SPIE.

关键词： Convolutional neural networks

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Lightweight Facial Expression Recognition Based on Joint Model Compression

Lightweight Facial Expression Recognition Based on Joint Mod...

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2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Song, Guangming Fan, Chunxiao School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230009 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

In this paper, we propose a lightweight facial expression recognition algorithm based on joint model compression. Firstly, we propose a self-distillation method based on facial expression recognition, which not only considers the overall output of the model but also focuses on the prediction results of each facial expression category, thereby improving the performance and generalization ability of the facial expression recognition model. Secondly, in order to further compress the facial expression recognition model, we apply a channel pruning method based on transformed L1 regularization. This pruning method imposes non-convex constraints on the parameters of the facial expression recognition model through transformations of the parameters in the L1 norm penalty term, achieving more accurate sparse regularization and effectively preventing overfitting of the model. Lastly, the pruned network is quantified to low-bit to obtain the final lightweight facial expression recognition model. The proposed method achieves 73.25% on the FER2013 dataset, with a parameter compression rate of approximately 50%, which achieves an effective lightweight. © 2025 SPIE.

关键词： Face recognition

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Teacher in the Loop: Customizing Educational Games Using Natural Language 17

Teacher in the Loop: Customizing Educational Games Using Nat...

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17th international conference on computer Supported Education, CSEDU 2025

作者： Bouali, Nacir Cavalli-Sforza, Violetta Tukiainen, Markku University of Twente Enschede7522 NB Netherlands School of Science and Engineering Al Akhawayn University in Ifrane Ifrane53000 Morocco School of Computing University of Eastern Finland Joensuu80101 Finland

ISBN: (纸本)9789897587467

Despite significant advances in educational technology and design methodologies, current educational games demonstrate a fundamental limitation: educators are unable to modify content after the games are deployed, limiting curriculum alignment and pedagogical customization. This paper introduces Imikathen-VR, a solution built upon a text-to-animation system, supporting K-1 and K-2 teachers to create minigames for their students to practice basic writing skills. Our implementation extends an existing animation pipeline by integrating a fine-tuned T5 model for sentence simplification, achieving 95% F1 BERT score and 76% ROUGE-L score in maintaining semantic and lexical fidelity. We improve visual reasoning by transforming the task of identifying missing visual details into a Masked Language Modeling problem. Preliminary results demonstrate the system's effectiveness in generating curriculum-aligned VR exercises, though comprehensive classroom testing remains pending. This work advances the integration of customizable VR technology in early education, providing teachers with enhanced control over educational content. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Teaching

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Gesture and Voice-Based Automation System for Home Surveillance 1

Gesture and Voice-Based Automation System for Home Surveilla...

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1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Santhosh, B. Oli, Sanjay Jayanth, C. Suresh Kumar, M. Raj, Preetham N Kandpal, Ankit Dayananda Sagar College of Engineering Department of Electronics and Telecommunication Engineering Bangalore560078 India Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India Dayananda Sagar College of Engineering Department of Information Science and Engineering Bangalore560078 India Bipin Tripathi Kumaon Institute of Technology Dwarahat Department of ECE Almora India

ISBN: (纸本)9798331518578

Gesture and voice-based automation innovations are being examined because of the developing interest for reliable and smart home security frameworks. In this study, a MATLAB-based home reconnaissance automation framework in light of voice and motion is created and reproduced. To perceive, interpret, and answer user developments and vocal orders, the proposed framework utilizes modern sign handling and AI calculations. Although nonverbal communication through gestures has been around for a while, gesture-based human-computer interaction (HCI) is growing in popularity in the present day. The conventional interface uses a number of tools, including markers, physical controllers, and gloves, to increase the recognition rate. This work offers a novel marker less method for capturing gestures without the use of expensive gear or any obstacles. In order to enable disabled individuals to operate household appliances using their own speech and gesture commands, we present in this paper a speech and Gesture Driven automation system for homes with a central controller. The framework joins programmed reactions with continuous observation highlights to further develop security, openness, and user solace. Framework reenactment, including control rationale, voice order handling, and motion identification, is finished with MATLAB. The accuracy, reactivity, and common sense of the innovation are shown by the recreation results. © 2025 IEEE.

关键词： MATLAB

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Integrating Big Data Analytics in Soil Quality Assessment: Techniques, Challenges, and Applications 3

Integrating Big Data Analytics in Soil Quality Assessment: T...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Hanif, Md Abu Lovely Professional University Department of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Department of Computer Science 365 5th Ave New YorkNY110016 United States

ISBN: (纸本)9798331515911

This review paper investigates the application of Big Data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big Data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big Data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big Data and conventional soil science practices.. © 2025 IEEE.

关键词： Data quality

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Development of intelligent Operation and Maintenance and Communication Devices for Distribution Network 13th

Development of Intelligent Operation and Maintenance and Com...

引用

13th international conference on Clean and Green Energy

作者： Zhong, Zhaobin Chen, Xiaojun Ye, Qing Zhang, Qiran Chen, Tao Chen, Junhong China Southern Power Grid Technol Co Ltd Guangzhou Peoples R China Guangdong Prov Key Lab New Technol Smart Grid Guangzhou Peoples R China

ISBN: (纸本)9789819618149;9789819618125;9789819618118

Distribution network automation equipment has been widely installed and used in the power grid, which plays a very important role in improving the reliability and quality of power supply. However, due to the wide range of distribution network automation equipment points, poor operating environment, and a large workload of inspection and maintenance of operating units, it is urgent to take the initiative to find defects through technical means, so as to warn and eliminate equipment defects and hidden dangers in advance, and achieve rapid fault location and isolation. In order to meet the requirements of transforming electric power network into digital, networked and intelligent new power system, an intelligent operation, maintenance and communication device of distribution network is proposed. The device supports wireless 5G, wired Ethernet, Bluetooth and other communication modes, and has a variety of communication interfaces such as RJ45, RS485 and RS232. It can adapt to various external devices according to application scenarios. The software supports intelligent operation and maintenance functions such as distribution network device status data collection, device defect management and control, and device local Bluetooth operation and maintenance, and supports IPSec tunnel encryption mode, which meets the intelligent operation and maintenance and encrypted communication requirements of distribution network automation devices, and effectively improves the digitalization and intelligent features of distribution network automation devices. The device has been tested by the third party testing organization and has high operating stability.

关键词： Distribution network Communication devices intelligent operation and maintenance

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Enhanced Multimodal Depression Detection With Emotion Prompts

Enhanced Multimodal Depression Detection With Emotion Prompt...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Teng, Shiyu Liu, Jiaqing Sun, Hao Chai, Shurong Tateyama, Tomoko Lin, Lanfen Chen, Yen-Wei College of Information Science and Engineering Ritsumeikan University Shiga Japan College of Computer Science and Technology Zhejiang University Hangzhou China Department of Intelligent Information Engineering Fujita Health University Japan

ISBN: (纸本)9798350368741

Depression is a pervasive mental health disorder that remains frequently undiagnosed and untreated due to societal barriers and the subjective nature of its symptoms. Leveraging recent advances in large language models (LLMs), we propose a novel depression detection pipeline that generates emotion prompts tailored to individual data, enhancing detection accuracy. Our approach integrates cross-modality fusion via cross attention mechanisms to combine depressive and emotional features, creating a comprehensive representation of depression indicators. Evaluated on the E-DAIC and EATD datasets, our method outperforms state-of-the-art techniques, demonstrating its potential for more precise emotion-based depression detection. © 2025 IEEE.

关键词： Depression detection Emotion prompt Large language model Multi-modal fusion

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advances in AI for Women’s Healthcare: A Review of Deep Learning and NLP Approaches 3

Advances in AI for Women’s Healthcare: A Review of Deep Lea...

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3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Choudhary, Jyoti Thakur, Madhuri School of Computer Science and Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798331527549

Women’s health faces critical challenges, particularly in maternal and reproductive healthcare, where complications significantly contribute to global mortality rates. Despite advancements in medical science, inequities in healthcare access and diagnosis persist, especially in underserved regions. Innovative approaches leveraging artificial intelligence (AI) technologies, such as deep learning (DL) and natural language processing (NLP), are transforming healthcare by enabling early diagnosis, personalized treatment, and scalable preventive care. This paper evaluates the transformative role of these AI-driven methods in addressing critical women’s health issues, including cancer detection, maternal health, and reproductive disorders, with a focus on advancements from 2018 to 2023. These years represent a period of rapid AI evolution and its integration into healthcare systems. The present study captures important challenges like restricted diversity of datasets, issues concerning transparency in models, and obstacles in actual deployment. It collates findings to give a global perspective of AI development in women's health. It is also prepared with practical future action steps for its research to lead AI-influenced innovations shaping the future of women's healthcare. © 2025 IEEE.

关键词： Deep learning

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Harnessing AI for Cybersecurity: Performance Evaluation of Machine Learning Techniques in Intrusion Detection Systems 1

Harnessing AI for Cybersecurity: Performance Evaluation of M...

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1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Naween Sharma, Supragya Gupta, Sahil Jain, Sajal Singh, Sujal School of Computer Science Engineering & Technology Bennett University Greater Noida India

ISBN: (纸本)9798331518578

Due to the current development of networked systems and the emergence of global cyber threats, efficient and fast NIDS are required. The suggested article aims to analyse new and improved AI and Machine Learning (ML) frameworks to design and implement efficient Network Intrusion Detection Systems (NIDSs) that accurately detect and differentiate network intrusions. The article assesses several effective algorithms for intrinsic and practical use: Logistic Regression, Random Forest, Feedforward Neural Networks (FNN), K Nearest Neighbours (KNN), and Long Short-Term Memory (LSTM), in terms of actual positive intrusions without creating high positive false intrusions. A comparative analysis is then made to evaluate the models based on precision, recall rate, accuracy, and the model processing rate. This suggested article presents a comparison of the two techniques regarding their effectiveness and applicability of the techniques for implementation in the next generation of NIDS. Although observations showed good performance and improvement regarding class imbalance, they remain key issues affecting such systems' reliability, including high computational demand and model interpretability. The reported results such as accuracy 99.897%, precision 99.894%, recall 99.898%, and F1 Score 99.892% in case of Logistic Regression and accuracy 99.882%, precision 99.876%, recall 99.882%, and F1 Score 99.892% in case of Random Forest from the work contribute to the advancement of fully intelligent NIDS that will improve the system stability to defend and adapting for new emerging threats, hence improving the advance of technologies in the complex and sophisticated era of cyberstalking. © 2025 IEEE.

关键词： Network intrusion

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