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Augmented Reality With Authentic GeometryGo App to Help Geometry Learning and Assessments

IEEE Transactions on Learning Technologies 2023年第5期16卷 769-779页

作者： Hwang, Wu-Yuin Nurtantyana, Rio Purba, Siska Wati Dewi Hariyanti, Uun Suprapto National Dong Hwa University Department of Computer Science and Information Engineering College of Science and Engineering Hualien974 Taiwan National Central University Graduate Institute of Network Learning Technology Taoyuan City320 Taiwan Telkom University School of Computing Bandung40257 Indonesia Universitas Pelita Harapan Department of Master of Educational Technology Kota Tangerang15811 Indonesia Brawijaya University Information Systems Department Kota Malang65145 Indonesia State University Yogyakarta Department of Electronics Engineering Education Yogyakarta55281 Indonesia

We developed an Authentic GeometryGo (AGG) app to help students application of geometry in authentic contexts through authentic measurements and peer assessments. The study included an experimental group (EG) using an AGG app and a control group (CG) using paper-based methods. The results showed that the EG outperformed the CG in terms of estimation. Measuring authentic geometry shapes using the app could foster students' estimation. Furthermore, meaningful peer assessments with visual representations in authentic contexts can facilitate the evaluation of peers' work and improve students' geometry understanding. Therefore, we encourage educators to use augmented reality with appropriate learning activities to sharpen students' measurement quality and visual representations by measuring real geometry shapes and applying mathematics knowledge in authentic contexts. © 2008-2011 IEEE.

关键词： Augmented reality

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Leveraging GPT-4 Capabilities for Developing Context-Aware, Personalized Chatbot Interfaces in E-commerce Customer Support Systems 10

Leveraging GPT-4 Capabilities for Developing Context-Aware, ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Santosh, Kathari Kholmukhamedov, Timur Kumar, M. Sandeep Aarif, Mohd Muda, Iskandar Bala, B Kiran Cmr Institute of Technology Bengaluru Department of Mba Bengaluru India Turin Polytechnic University In Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan Publishing Global Research Network Noida India Universitas Sumatera Utara Medan Indonesia K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350353068

In the realm of e-commerce customer support, the adoption of chatbots is on the rise, driven by a quest for heightened user interactions. This study introduces an inventive approach harnessing the advanced capabilities of GPT-4 to construct chatbot interfaces that are both context-aware and personalized. The main aim of this work is to revolutionize ecommerce customer support by developing intelligent and adaptable chatbot interfaces that are context-aware, personalized, and significantly enhance user experiences. Existing chatbot models struggle with context retention and personalization, limiting effectiveness in dynamic e-commerce environments despite the promise of automation. This innovative approach leverages GPT-4's robust language processing, integrating it with user profiling systems for personalized responses grounded in user preferences and historical interactions. The method gives, diverse e-commerce dataset undergoes collection and pre-processing, followed by fine-tuning GPT-4 with an emphasis on context-awareness and personalized responses. The model is seamlessly integrated with e-commerce backend systems for real-time information, multimodal input support caters to varied user preferences. Results exhibit a significant 95% accuracy improvement, affirming the chatbot's enhanced ability to comprehend user queries. By integrating personalization and advanced context retention, this study enhances user engagement, paving way for revolutionary intelligent and adaptable chatbot interfaces in ecommerce customer support. © 2024 IEEE.

关键词： Electronic commerce

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Using Real-Time Integrated computer Vision and Deep Learning for Advanced Factory Safety 4

Using Real-Time Integrated Computer Vision and Deep Learning...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Tsai, Shang-Te Chang, Yu-Cheng Huang, Chao-Yi Yang, Fang-Yuan Chien, Wei Wu, Zong-Rong School Art and Design Sanming University Fujian China National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan National Yunlin University of Science and Technology Department of Information Management Yunlin Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan National Yunlin University of Science and Technology Department of Technical and Vocational Program in Artificial Intelligence Yunlin Taiwan

ISBN: (纸本)9798350360721

Innovative safety in the workplace is vital as the high safety risks associated with electrical engineering construction can lead to injuries or even fatalities. Using computer vision technology, we experimented with scenarios such as 'normal operations' and 'unexpected incidents' to enhance safety measures. We integrated an Internet of Things system into the setup, enabling the system to quickly detect and alert to unexpected events in real-time, thereby improving workplace safety. © 2024 IEEE.

关键词： computer vision

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Comparative Analysis of Real-Time Multi-Person Pose Detection in Electrical Industrial Safety Scenarios Using YOLOv8-Pose and OpenPose 4

Comparative Analysis of Real-Time Multi-Person Pose Detectio...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Tsai, Shang-Te Wu, Zong-Rong Lin, Pin-Hsuan Chen, Chin-Hsiung Chien, Wei Chang, Yu-Cheng School of Art and Design Sanming University Fujian365004 China National Yunlin University of Science and Technology Department of Technical and Vocational Program in Artificial Intelligence Yunlin Taiwan National Central University Department of Business Administration Taoyuan Taiwan Commercial Industrial Vocational Senior High School Department of Electrical Engineering New Taipei Municipal Tamsui New Taipei Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350360721

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy. © 2024 IEEE.

关键词： Accident prevention

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Evaluation Model of Learner's Cognitive Level Based on RoBERTa Fused with CNN 3

Evaluation Model of Learner's Cognitive Level Based on RoBER...

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3rd International Conference on Neural networks, Information and Communication engineering, NNICE 2023

作者： Cai, Yingying Zhao, Yuancheng Luo, Haibo Huang, Zhongyou Huang, Zhongying Zhang, Feng Neusoft Institute Guangdong Department of Computer Network Engineering Foshan China Gd Midea Heating & Ventilating Equipment Co. Ltd Advanced Research Center Foshan China

ISBN: (纸本)9798350335972

The cognitive level is an essential indicator of the learning process evaluation. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which cannot fully mine the implicit cognitive semantic information in unstructured text data. In this study, a fusion model named RoBERTa-CNN is proposed, which uses RoBERTa pre-training model to capture the cognitive semantic features implied between words in the sentence. Furthermore, it combines a convolution neural network to extract the global semantic features of sentences, to extract the deep-level features of the text. Using Bloom's cognitive goal classification theory as the framework of cognitive level evaluation, 9657 text data from MOOC were evaluated automatically. The results show that the mixed RoBERTa-CNN model has the best evaluation effect, and the overall accuracy rate of the six cognitive levels is 85.26%, demonstrating the model's validity. © 2023 IEEE.

关键词： Semantics

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Multiresource allocation in dynamic MEC systems: a continual learning-based approach 3

Multiresource allocation in dynamic MEC systems: a continual...

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3rd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Zhan, Shutao Mao, Jin Chu, Zhipeng Xiong, Ke Fan, Pingyi Letaief, Khaled B. Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781510680449

Recently, mobile edge computing (MEC) has been considered as a promising solution to address the challenges arising from the growing computationally intensive applications and limited computation capability of mobile devices. Thus, the resource allocation is crucial for ensuring the system performance of MEC. Recent deep learning-based resource allocation (DLRA) methods achieve preferable performance in real-time. However, in practical scenarios, the number of users often changes, which leads to a decrease in the performance of DLRA methods. Thus, once the number of users changes, the traditional DLRA methods need to retrain their neural networks, which consumes a lot of computing resources. To address this issue, this paper proposes a continual learning-based resource allocation (CLRA) method to jointly optimize multiple resource including offloading decision, power control, time slot assignment and computation resource distribution in MEC system. Our CLRA method is able to avoid forgetting previous knowledge when the number of users in the MEC system changes by selecting a small number of the most representative samples from the past and relearning. Experimental results show that, our proposed CLRA method achieves a better performance than the traditional DLRA method in terms of the higher quality of service's (QoS's) satisfaction rate and the less energy consumption. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Resource allocation

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Contextualized and Personalized Math Word Problem Generation Using GPT and Authentic Contextual Recognition 12

Contextualized and Personalized Math Word Problem Generation...

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12th International Conference on Information and Education Technology, ICIET 2024

作者： Hwang, Wu-Yuin Utami, Ika Qutsiati National Dong Hwa University Graduate Institute Of Network Learning Technology National Central University Department Of Computer Science And Information Engineering Taiwan Graduate Institute Of Network Learning Technology National Central University Taoyuan Taiwan Universitas Airlangga Faculty Of Advanced Technology And Multidiscipline Indonesia

ISBN: (纸本)9798350371772

This paper introduced an interactive system for generating contextualized and personalized mathematic word problems (MWP) from authentic contexts using the Generative Pre-trained Transformers (GPT). Our proposed Automatic Question Generation (AQG) system comprises (1) the authentic contextual information acquisition through image recognition by TensorFlow and augmented reality (AR) measurement through AR Core, (2) a personalized mechanism based on instructional prompts to generate three difficulty levels for learner's different needs, and (3) MWP generation through GPT with authentic contextual information and personalized needs. A quasi-experiment was conducted by recruiting 51 fifth-grade students to evaluate the effectiveness of the proposed AQG on their geometry learning performance. The results revealed that students who learned with the proposed AQG outperformed students who learned with a decontextualized way on geometry learning performances. Therefore, our proposed AQG is useful for promoting mathematic problem-solving activity in an authentic context. © 2024 IEEE.

关键词： Augmented reality

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Blockchain for 5G-Enabled IoHT—A Framework for Secure Healthcare Automation

Blockchain for 5G-Enabled IoHT—A Framework for Secure Healt...

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International Conference on Data Analytics and Management, ICDAM 2022

作者： Alam, Md Imran Ahmad, Md Oqail Siddiqui, Shams Tabrez Khan, Mohammad Rafeek Khan, Haneef Qidwai, Khalid Ali Department of Computer and Network Engineering Jazan University Jazan45142 Saudi Arabia Department of Computer Applications B.S Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai600048 India Department of Computer Science Jazan University Jazan45142 Saudi Arabia

ISBN: (纸本)9789811976148

Medical care has evolved into a lifeline for many people;due to this, there is an explosion in the field of medical big data. Wearable technology based on the Internet of Things (IoT) is being used by healthcare professionals to speed up diagnosis and treatment. There are now numerous sensors and devices, which are connected to the Internet. Internet of HealthThings (IoHT) can be used in many different ways in healthcare. Sensors and remote-monitored medical devices can help patients get more accurate and efficient diagnostics. This paper focuses on healthcare automation using the Internet of Things (IoT)-enabled devices. IoT-driven healthcare models can keep patients healthy and safe, enabling doctors to provide better care. In addition, healthcare frameworks significantly reduce healthcare costs and improve performance. Blockchain systems are transparent and thus safe to store and share private information. Data collected by medical devices attached to the body is kept in a cloud server alongside relevant medical reports as part of the proposed framework. The use of blockchain and 5G technology means that patients’ data can be securely transmitted at high speeds and with rapid response times. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Semi-supervised Medical Image Segmentation via Feature-perturbed Consistency

Semi-supervised Medical Image Segmentation via Feature-pertu...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Yang Wang, Ruixuan Zhang, Tong Su, Jingyong Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Peng Cheng Laboratory Department of Network Intelligence Shenzhen China Sun Yat-sen University School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350337488

Although deep convolutional neural networks have achieved satisfactory performance in many medical image segmentation tasks, a considerable annotation challenge still needs to be solved, which is expensive and time-consuming for radiologists. Most existing popular semi-supervised methods mainly impose data-level perturbations (e.g., rotation, noising) or feature-level perturbations (e.g., MC dropout) on unlabeled data. In this paper, we propose a novel semi-supervised segmentation strategy with meaningful perturbations at the feature level to leverage abundant useful information naturally embedded in the unlabeled data. Specifically, we develop a dual-task network where the segmentation head produces multiple predictions with a perturbation module, and the reconstruction head further utilizes the semantic information to enhance segmentation performance. The proposed framework subtly perturbs the network at the feature-level to generate predictions which should be similar and consistent. However, enforcing them roughly to be consistent at all pixels harms stable training and neglects much delicate information. To better utilize those predictions and estimate the uncertainty, we further propose feature-perturbed consistency to exploit reliable regions for our framework to learn from. Extensive experiments on the public BraTS2020 dataset and the 2017 ACDC dataset confirm the efficiency and effectiveness of our method. In particular, the proposed method demonstrates remarkable superiority in the segmentation of boundary regions. The project is available at https://***/youngyzzZ/SFPC. © 2023 IEEE.

关键词： Image segmentation Semi-supervised learning Uncertainty estimation

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Resource Allocation for Throughput Maximization in Cognitive Radio network with NOMA

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computers, Materials & Continua 2022年第1期70卷 195-212页

作者： Xiaoli He Yu Song Yu Xue Muhammad Owais Weijian Yang Xinwen Cheng School of Computer Science Sichuan University of Science and EngineeringZigong643000China Department of Network Information Management Center Sichuan University of Science and EngineeringZigong643000China School of Information Engineering South West University of Science and TechnologyMianyang621010China School of Computer and Software Nanjing University of Information Science and TechnologyJiangsu210044China Alhamd Islamic University Airport Road Quetta BalochistanPakistan

Spectrum resources are the precious and limited natural *** order to improve the utilization of spectrum resources and maximize the network throughput,this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonalmultiple access(CRN-NOMA).NOMA,as the key technology of the fifth-generation communication(5G),can effectively increase the capacity of 5G *** optimization problem proposed in this paper aims to maximize the number of secondary users(SUs)accessing the system and the total throughput in the *** the constraints of total power,minimum rate,interference and SINR,CRN-NOMA throughput is maximized by allocating optimal transmission ***,for the situation of multiple sub-users,an adaptive optimization method is proposed to reduce the complexity of the optimization ***,for the optimization problem of nonlinear programming,a maximization throughput optimization algorithm based on Chebyshev and convex(MTCC)for CRN-NOMA is proposed,which converts multi-objective optimization problem into single-objective optimization problem to *** the same time,the convergence and time complexity of the algorithm are *** analysis and simulation results show that the algorithm can effectively improve the system *** terms of interference and throughput,the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access(OFDMA).This paper provides important insights for the research and application of NOMA in future communications.

关键词： Resource allocation non-orthogonal multiple access cognitive radio network throughput maximization Chebyshev convex

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