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检索条件"任意字段=5th International Conference on Learning and Teaching in Computing and Engineering, LaTiCE 2017"

共 328 条记录，以下是21-30 订阅

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A Summary of the Research Methods of Artificial Intelligence in teaching 5th

A Summary of the Research Methods of Artificial Intelligence...

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

作者： Ge, Huiyao Zhu, Xiaoxiao Jiang, Xiaoyan School of Mathematics and Information Science Nanjing Normal University of Special Education Nanjing210038 China

ISBN: (纸本)9783031505799

With modern technology developing rapidly, "Artificial Intelligence" becomes a hot word of the times. the integration of the development of information technology and artificial intelligence provides an opportunity for education optimization. this article briefly reviews the application of artificial intelligence in teaching from four aspects: learning environment creation, learning data analysis, learning resource matching, and learning path intervention. through the creation of learning environment, it can broaden the learning dimension and help students to learn immersive. through intelligent analysis such as multimodal data mining and affective computing learning analysis, it can identify students’ emotional feedback for a certain content and help teachers adjust teaching content and progress with strong pertinence. learning resource matching technology helps to match learning resources according to students’ personality characteristics and appearance differences. Teachers can carry out learning path intervention for different students and help students to adjust their learning paths and consolidate knowledge learning. Some future research directions are proposed for some research methods. this will help relevant researchers to grasp the research in this field as a whole and play an important role in promoting the application and development of artificial intelligence in the field of education. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Students

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Enhancing Online learning: A Pose and Emotion-Based Approach for Student Attention Monitoring 5th

Enhancing Online Learning: A Pose and Emotion-Based Approac...

引用

5th international conference on computing and Network Communications, CoCoNet 2023

作者： Chingapurathu, Sarath Krishna Jeganathan, L. Janaki Meena, M. School of Computer Science and Engineering Vellore Institute of Technology TN Chennai600127 India

ISBN: (纸本)9789819747108

With the increasing popularity and preference of online classes, teachers are at a disadvantage of losing face-time with students and are faced with the challenge of monitoring their students’ attention levels remotely. this research, VisioFocus, presents a non-invasive student monitoring system using deep learning and computer vision to measure and analyze the attention levels of students. It has two main components namely Pose Estimation and Emotion Recognition which processes the captured facial images of the student through the webcam at 15 frames per second to measure the attention level. the Pose Estimation model uses pure computer vision and the Emotion Recognition model uses a modified ResNet deep convolutional network that could classify emotions portrayed into 6 different labels at about 70% accuracy. this research proposes a novel and reliable approach to measuring attention levels, which will have a significant impact on the education field. the analytical survey of attention levels after each lecture will enable educators to tailor their teaching approach to specific students, resulting in better academic development. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Identifying career pathways to engineering education research in Australia 5

Identifying career pathways to engineering education researc...

引用

5th international conference of the Portuguese Society for engineering Education, CISPEE 2023

作者： Valentine, Andrew Williams, Bill University of Melbourne Computing and Information Systems Melbourne Australia University of Lisbon CEGIST Lisbon Portugal TUDublin CEGIST Dublin Ireland

ISBN: (纸本)9798350315813

Currently there is limited understanding about the academic qualifications (or formal research training) which engineering education researchers possess, and which university departments they primarily reside in. the objective of this study is to capture information on these researcher characteristics via bibliographic analysis of publicly available sources.A list of 104 authors affiliated with Australian institutions who published in at least one of thirteen engineering education journals between 2018-2019 (inclusive) was retrieved. For each author, information about their qualifications and where they worked was compiled from available biographic information in their publications (e.g. often common in IEEE publications), ORCID profile, Scopus profile, the Australian TROVE database, and online university researcher profile. In total, 80 authors held a known PhD;30 in technical engineering, 12 in engineering education, 3 in education, 21 in other disciplines, and 14 not specified. Of the 67 with known bachelor degrees, 69% were in engineering, the remainder widely varied. 92 authors worked in a university;56 in engineering faculty, 9 in computing faculty, 13 in other faculty, 9 in teaching and learning departments, 4 in other capacities at a university. 4 authors did not work at a *** findings show that while a minority of engineering education researchers hold technical engineering PhD degrees, PhD theses on engineering education topics are becoming more widespread and we can predict a growing community of Australian engineering researchers. these data can assist with planning strategies for further increasing engagement with engineering education research in the Australian context. © 2023 IEEE.

关键词： engineering education

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Role of learning Management System for Evaluating Students' progress in learning Environment 5

Role of Learning Management System for Evaluating Students' ...

引用

5th international conference on Contemporary computing and Informatics, IC3I 2022

作者： Pal, Neeti Dahiya, Omdev Lovely Professional University School of Computer Applications Punjab India Lovely Professional University School of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350398267

the paper contributes to getting familiar with various tools and platforms incorporated with learning dashboards that provide digital teaching and learning. the article also includes the different components for learning analytics used by various universities in their LMSs dashboards to improve their teaching-learning achievements. learning analytics dashboards plays a vital role in the virtual learning environment. these tools work by tracking learners' data, analyzing data, and presenting the results by revealing the patterns of learners' behavior and attitude. this paper contributes to an analysis of learning dashboards and tools. the article also explains how these tools track, extract and analyze the students' learning progress and monitor their activities. Various visualization techniques are also discussed in this paper. this study will help determine the impact of using learning tools to evaluate learners' learning outcomes. © 2022 IEEE.

关键词： Computer aided instruction

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5th international conference on Recent Innovations in computing, ICRIC 2022

5th International Conference on Recent Innovations in Comput...

引用

5th international conference on Recent Innovations in computing, ICRIC 2022

ISBN: (纸本)9789811998751

the proceedings contain 51 papers. the special focus in this conference is on Recent Innovations in computing. the topics include: Educational Robotics in teaching Programming in Primary School;evolution of Autonomous Vehicle: An Artificial Intelligence Perspective;systematic Review of learning Models for Suicidal Ideation on Social Media;A Novel Recommendation System for Mutual Management on Both OTT Video Streaming and Advertising Using Incremental learning Approach;Early Diagnosis of Cervical Cancer Using AI: A Review;preface;brain Tumor Segmentation Using Fully Convolution Neural Network;exploring Data-Driven Approaches for Apple Disease Prediction Systems;deep learning Techniques Used for Fake News Detection: A Review and Analysis;darkness Behind the Darknet: A Comparative Analysis of Darknet Traffic and the Crimes Behind the Darknet Using Machine learning;development of Chatbot Retrieving Fact-Based Information Using Knowledge Graph;short-Term Load Demand Forecasting Using Artificial Neural Network;comparative Analysis of Performances of Convolutional Neural Networks for Image Classification Tasks;Detection of Android Malwares on IOT Platform Using PCA and Machine learning;A Survey on Different Security Frameworks and IDS in Internet of things;emerging IoT Platforms Facilitate Entrepreneurship Businesses;navigation and Cognitive Techniques for Humanoid Robots;artificial Intelligence-Based Lung Nodule Detection: A Survey;an IoT-Based Approach for Visibility Enhancement and Fog Detection;Internet of Medical things (IoMT) Application for Detection of Replication Attacks Using Deep Graph Neural Network;A Review on GIF-Based Steganography Techniques;Optimization and Design of Efficient D Flip-Flops Using QCA Technology;analysis of Breast Cancer Prediction Using Machine learning Techniques: Review Paper.

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Research of Hybrid Collaborative Filtering Algorithm in Course Recommendation 5

Research of Hybrid Collaborative Filtering Algorithm in Cour...

引用

5th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Wang, MeiRong Mariano, Vladimir Y. College of Computing and Information Technologies National University Manila Philippines College of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China

ISBN: (纸本)9798350357950

At present, as people's awareness of learning increases, more and more learning resources appear on the Internet, and online courses are becoming more and more popular. Recommendation research for online courses can help users get the quality courses they need faster. this article integrates the optimized Apriori algorithm and the user-based collaborative filtering algorithm and recommends more appropriate courses to users based on this hybrid model. the results are more reasonable than those obtained by relying on any of the above algorithms alone. © 2023 IEEE.

关键词： optimized Apriori recommendation user-based collaborative filtering algorithm

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A report on the fifth workshop on software engineering education (SEED 2022) 2022

A report on the fifth workshop on software engineering educa...

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15th Innovations in Software engineering conference, ISEC 2022

作者： Kumar, Kuldeep Suri, Bharti Wadhwa, Bimlesh Dr B R Ambedkar National Institute of Technology India Jalandhar India University School of Information and Communication Technology Guru Gobind Singh Indraprastha University Delhi India School of Computing National University of Singapore Singapore

ISBN: (纸本)9781450396189

the 5th international Workshop on Software engineering Education (SEED 2022), co-located with the 15th Innovations in Software engineering conference (ISEC 2022), aims to provide a unique forum to bring together researchers, educators, students, and practitioners to report on their experiences and their ongoing efforts in meeting the recent demands of remote teaching and learning in Software engineering. the theme of SEED 2022 is Software engineering Education amid a global pandemic - How can software engineering teaching meet the challenge of the sudden shift to online education triggered by the COVID-19 pandemic? Strategies for project-based learning, hybrid learning, blended learning, use of tools in teaching and learning are specifically targeted in this workshop. Further, it aims to provide a unique opportunity to Software engineering educators and practitioners to come together and build collaborations for Software engineering education research and practice. © 2022 Owner/Author.

关键词： Software design

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Machine-learning-based network sparsification modeling for IoTs security analysis 5

Machine-learning-based network sparsification modeling for I...

引用

2023 5th international conference on Information Science, Electrical, and Automation engineering, ISEAE 2023

作者： Ling, Mingcheng Qi, Weimin Chang, Di Zhang, Xia Zhang, Chi Engineering Training Center Jianghan University Hubei Wuhan China School of Artificial Intelligence Jianghan University Hubei Wuhan China Department of Science and Innovation U.S. Xpress Inc. AtlantaGA United States

ISBN: (纸本)9781510667440

As the security issues of Internet of things (IoTs) are rapidly evolving, machine learning techniques are increasingly adopted for detecting and preventing cyber threats. Recent machine learning based approaches (e.g., anomaly detection, intrusion detection, and predictive analytics) are being utilized in IoTs security. With the proliferation of IoTs devices, it is crucial to develop scalable and effective security solutions to keep pace with the changing threat landscape. this paper proposes a novel NSM (Network Sparsification Modeling) approach for identifying and categorizing cybersecurity threats in the cloud and IoTs environment. the proposed NSM algorithm is to optimize the Kullback-Leilber divergence based on higher-order spanning k-tree modeling process. the NSM model is capable of detecting cybersecurity threats in the cloud and IoTs setting by converting raw data into a meaningful format. the performance of the NSM model was evaluated using CICIDS 2017 dataset. the testing results prove that NSM model is state-of-the-art by outperforming others. Future deep-learning approaches are capable to integrate in the ML-based NSM model for further enhancement. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Cybersecurity

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Improving Automated Assessment of English Spoken Discourse Using Transfer learning Models-Wav2Vec 2.0 5

Improving Automated Assessment of English Spoken Discourse U...

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5th IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Hameed, N. Sheik Vijayakumar, S. Rajaraman, K. Kumar, V. Vinod Tamilarasan, P. Faizal, M. K. Mohamed B. S Abdur Rahman Crescent Institute of Science and Technology Department of English Chennai India Annamalai University Department of English Tamil Nadu Chidambaram India Bharathidasan University Department of English Tiruchirappalli India Srm University Department of English Chennai India K.Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350356236

Automated Assessment of English Spoken Discourse is invaluable tool used in the process of learning foreign languages, teaching and communication as it is capable of delivering a large number of assessments of spoken language without significant human interferences. However, common approaches may yield some issues in terms of accuracy, methods' applicability to a variety of accents and languages, and the scalability of the techniques. Conventional techniques including HMM-GMM and contemporary DL techniques like the CNN-RNN triangulate unsatisfactory results due to the inability to capture more intricate spoken discourse since they are unable to pick tune from the din. In response to these difficulties, this research proposal presents the following framework using Wav2Vec 2.0, a self-supervised learning model involved in speech signals. the proposed method makes use of Wav2Vec 2.0 to capture rich representations directly from the raw input data to minimize dependence on labelled data and improve robustness to different patterns of speech. Applicable in Python, the model is fine-tuned in spoken discourse datasets and is assessed based on primary metrics of performance;test performance: 99.1%. these results are much better than traditional and baseline methods and clearly demonstrate the high stability and potential of the model. this way, the study that addresses prior limitations and offers a stable solution contributes to the development of the automated speech assessment. Further work that could be suggested is the selection of multimodal integration approaches and real time as a way to increase more the scores of the different models and the flexibility of the system that has been proposes in education and work environments. © 2025 IEEE.

关键词： Self-supervised learning

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Knowledge Distillation Scheme for Named Entity Recognition Model Based on BERT 5

Knowledge Distillation Scheme for Named Entity Recognition M...

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5th international conference on Machine learning, Big Data and Business Intelligence, MLBDBI 2023

作者： Ye, Chengqiong Hernandez, Alexander A. National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei China

ISBN: (纸本)9798350359930

In order to improve the applicability of named entity recognition model in industrial application scenarios, this paper studies the BERT named entity recognition task. From the perspective of lightweight model, an adaptive weight distillation method is designed to compress the BERT named entity recognition model. By adjusting the weight hyperparameter of distillation loss to an adaptive function, this method enables the model to automatically adjust the weight value according to the training process, avoiding the influence of manual parameter adjustment. Furthermore, by decreasing the middle hidden layer's dimension, this method compresses the teacher model from the width and incorporates the distillation of the middle Transformer layer of the model into the distillation process, allowing it to fully utilize the rich semantic information in the deep structure of the model. Ultimately, a light-weight, well-functioning model with a straightforward structure and few parameters is trained. By employing the suggested technique, the model's inference speed can be increased by seven times while reducing the number of model parameters to 15% of the original model with little loss of accuracy. © 2023 IEEE.

关键词： Distillation

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