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检索条件"任意字段=International Conference on Learning and Teaching in Computing and Engineering"

共 108002 条记录，以下是331-340 订阅

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Algorithm for Resource Allocation and computing Offloading in 6G Networks: Deep Reinforcement learning-based 9

Algorithm for Resource Allocation and Computing Offloading i...

引用

9th international conference on Mechatronics engineering (ICOM)

作者： Saeed, Mamoon M. Saeed, Rashid A. Ali, Elmustafa Sayed Mokhtar, Rania A. Khalifa, Othman O. Univ Modern Sci UMS Fac Engn Dept Commun & Elect Engn Sanaa Yemen Taif Univ Coll Comp & Informat Technol Dept Comp Engn POB 11099 Taif 21944 Saudi Arabia Red Sea Univ Fac Engn Dept Elect & Elect Engn Portsudan Sudan Sudan Univ Sci & Technol Fac Engn Dept Elect Engn Khartoum Sudan Inter Islamic Univ Malaysia Dept Elec & Comp Engn Kl Malaysia Libyan Ctr Engn Res & Info Tech Bani Walid Libya

ISBN: (纸本)9798350349795;9798350349788

The emergence of the sixth generation (6G) wireless networks brings new challenges and opportunities for efficient computing offloading and resource allocation. This paper proposes a novel Deep Reinforcement learning-based computing Offloading and Resource Allocation (DRL-CORA) algorithm for 6G networks. The algorithm leverages the power of deep reinforcement learning to dynamically determine the optimal computing offloading decisions and resource allocation strategies. The Deep Reinforcement learning-based DCORA algorithm for computation offloading and resource allocation is effective, as demonstrated by our simulations. When compared directly, the suggested DCORA algorithm performs 15% better than other baseline systems.

关键词： Deep reinforcement learning resource allocation network of 6G computer offloading mobile edge computing

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Social computing Interactions and Fusion AI Development for teaching and Entertainment 10

Social Computing Interactions and Fusion AI Development for ...

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10th international conference on engineering and Emerging Technologies, ICEET 2024

作者： Tawafak, Ragad M Alfarsi, Ghaliya Alsideiri, Abir Iqbal, Sohail Malik Mathew, Roy Buraimi Oman

ISBN: (纸本)9798331532895

Integrating social computing with a combination of AI improvement offers transformative potential for both instructing and exciting applications. This paper investigates how social computing-characterized by collaborative and intelligent client behavior in advanced environments-can be improved through AI advances to make more personalized, engaging, and versatile encounters. Within the instruction set, AI-driven social computing frameworks empower clever coaching, real-time input, and peer collaboration, cultivating higher levels of understudy engagement and personalized learning pathways. In amusement, AI-powered social stages can create immersive, intelligently encounters custom-fitted to person inclinations, advertising unused shapes of substance creation and client support. Be that as it may, whereas these innovations hold excellent guarantees, they, too, show critical challenges, such as guaranteeing client security, tending to moral concerns, and planning frameworks that viably adjust human and machine intelligence. This paper analyzes the benefits, potential applications, and challenges of blending social computing with AI within instruction and excitement, giving a system for future inquiry about and advancement in this advancing space. © 2024 IEEE.

关键词： Entertainment

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Mapping Kolb's learning Style to Roles in Software Development Team 50

Mapping Kolb's Learning Style to Roles in Software Developme...

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50th Latin American computing conference (CLEI)

作者： Hidalgo, Mauricio Rodriguez, Kattia Astudillo, Hernan Univ Tecn Federico Santa Maria FDI Univ Finis Terrae Santiago Chile Univ Tecn Nacl Villa Bonita Costa Rica ITiSB Univ Andres Bello Vina Del Mar Chile

ISBN: (纸本)9798331540982;9798331540975

The effective transfer and acquisition of necessary knowledge, methods, and attitudes pose significant challenges for Software engineering Education. Furthermore, training in software development skills and knowledge currently lacks a clear set of techniques to link learning styles and preferences with development team roles. This paper characterizes the learning style of four traditional roles in software development (Analyst, Architect, Developer, and Project Manager) using Kolb's learning Styles Inventory. Kolb's learning Styles Test was administered to 110 software development practitioners (15 analysts, 18 architects, 50 developers, and 27 project managers). The test results show that, with some differences, architects and analysts have the Deciding style, while developers and project managers exhibit the Thinking style. Finally, in alignment with Kolb's learning strengths and challenges, this work provides a set of teaching strategies for each role based on their inferred learning styles.

关键词： Software engineering Education Software Development Roles learning Styles Kolb's learning Styles Inventory

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Empowering Diverse Learners: Embracing Culturally Relevant Pedagogy (CRP) in engineering, Higher Education, and K-12 Settings 26th

Empowering Diverse Learners: Embracing Culturally Relevant P...

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26th international conference on Interactive Collaborative learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： Wahed, Shabnam Pitterson, Nicole Virginia Tech Engn Educ Blacksburg VA 24061 USA

ISBN: (纸本)9783031530210;9783031530227

Culturally relevant pedagogy (CRP) is an approach to teaching and learning that recognizes the cultural backgrounds and experiences of students and seeks to use them as a foundation for education. Empowering diverse learners through the embrace of CRP is significant because it recognizes the importance of valuing and incorporating the cultural diversity of students in the teaching and learning process. By embracing CRP, educators can create inclusive and supportive learning environments that validate and respect the diverse cultural backgrounds of students. This pedagogical approach can lead to greater academic success, improved well-being, and increased engagement among students from diverse backgrounds. The purpose of this review paper is to investigate the usage of CRP in different educational settings by synthesizing and summarizing existing literature on the topic. The work demonstrates how embracing CRP in engineering, higher education, and K-12 settings can lead to inclusive and supportive learning environments that validate and respect the diverse cultural backgrounds of students. It also showcases the potential positive outcomes of CRP, including greater academic success, improved well-being, increased engagement, higher retention rates, transformative practices, critical consciousness, social justice, and closing the achievement gap. By promoting cultural awareness, respect for diversity, and equity, the paper advocates for the adoption of CRP to benefit students and society as a whole.

关键词： Culturally relevant pedagogy engineering Higher education K-12

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Knowledge Graph Completion Based on Contrastive learning for Diet Therapy 27

Knowledge Graph Completion Based on Contrastive Learning for...

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27th IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed computing (SNPD)

作者： Yang, Kaidi Lin, Yangguang Mi, Xuanhan Li, Yuxun Lin, Xiao Li, Dongmei Beijing Forestry Univ Engn Res Ctr Forestry Oriented Intelligent Inform Natl Forestry Beijing 100083 Peoples R China

ISBN: (纸本)9798350391961;9798350391954

In recent years, an increasing number of individuals have turned to traditional Chinese medicine diet therapy as a means to nourish their bodies and mitigate diseases. With the advent of the big data era, knowledge graphs, as powerful analysis tools, can provide more accurate and personalized dietary advice for diet therapy. However, most of the current diet therapy knowledge graphs have imperfections. To address this issue, we construct a diet therapy knowledge graph by utilizing textual data and professional books provided by the Academy of Traditional Chinese Medicine, from which we extract entities and relations. Building upon this foundation, we introduce a text representation technique predicated on contrastive learning, designed to augment the semantic richness of the knowledge graph and enhance the completion of the diet therapy knowledge graph. By conducting experiments on the diet therapy knowledge graph and public datasets, the results show that our method can capture the semantic information in the knowledge graph more efficiently compared to traditional methods. This provides new possibilities for research and practice in the field of traditional Chinese medicine diet therapy. This research opens new avenues for leveraging big data analysis in traditional Chinese medicine diet therapy.

关键词： knowledge graph completion diet therapy big data contrastive learning

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Exploration of OBE-Based Competence Development Model for Undergraduate Computer Science Students 5th

Exploration of OBE-Based Competence Development Model for Un...

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5th international conference on Computer Science and Educational Informatization, CSEI 2023

作者： Luo, Juan Qiao, Ying Xiao, Degui Zhao, Huan College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9789819994984

The inconsistency and lack of synchronization between computer professional competence education and talents' market demand are the bottleneck problems of undergraduate education. In this paper, we establish a "student-centered" output teaching model based on OBE concept, and construct a computer core competence model based on KSD. We propose a personalized teaching system based on the knowledge graph of teaching contents and students’ learning trajectories. It continuously improves and optimizes the training plan according to students’ learning outcomes, and improves and adjusts the course contents, which achieves the cultivation of undergraduate students’ ability to solve complex engineering problems. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Education computing

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Enhancing End-to-End Multi-Task Dialogue Systems: A Study on Intrinsic Motivation Reinforcement learning Algorithms for Improved Training and Adaptability 18

Enhancing End-to-End Multi-Task Dialogue Systems: A Study on...

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18th IEEE international conference on Semantic computing (ICSC)

作者： Kamuni, Navin Shah, Hardik Chintala, Sathishkumar Kunchakuri, Naveen Alla, Sujatha BITS Pilani WILP AI ML MTech Pilani Rajasthan India Rochester Inst Technol Dept IT Rochester MN USA Fisher Investments Dept IT San Mateo CA USA Eudoxia Resarch Univ Dept IT Wilmington DE USA Old Dominion Univ Norfolk VA USA

ISBN: (纸本)9798350385359

End-to-end multi-task dialogue systems are usually designed with separate modules for the dialogue pipeline. Among these, the policy module is essential for deciding what to do in response to user input. This policy is trained by reinforcement learning algorithms by taking advantage of an environment in which an agent receives feedback in the form of a reward signal. The current dialogue systems, however, only provide meagre and simplistic rewards. Investigating intrinsic motivation reinforcement learning algorithms is the goal of this study. Through this, the agent can quickly accelerate training and improve its capacity to judge the quality of its actions by teaching it an internal incentive system. In particular, we adapt techniques for random network distillation and curiosity-driven reinforcement learning to measure the frequency of state visits and encourage exploration by using semantic similarity between utterances. Experimental results on MultiWOZ, a heterogeneous dataset, show that intrinsic motivation-based debate systems outperform policies that depend on extrinsic incentives. By adopting random network distillation, for example, which is trained using semantic similarity between user-system dialogues, an astounding average success rate of 73% is achieved. This is a significant improvement over the baseline Proximal Policy Optimization (PPO), which has an average success rate of 60%. In addition, performance indicators such as booking rates and completion rates show a 10% rise over the baseline. Furthermore, these intrinsic incentive models help improve the system's policy's resilience in an increasing amount of domains. This implies that they could be useful in scaling up to settings that cover a wider range of domains.

关键词： Dialogue Systems MultiWOZ Proximal Policy Optimization Reinforcement learning Web Accessibility

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Application and Practice of Smart Education Technology in Blended teaching of engineering Drawing Course 24

Application and Practice of Smart Education Technology in Bl...

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9th international conference on Distance Education and learning, ICDEL 2024

作者： Xu, Jinyou Tian, He Wang, Xiaofei Xue, Yahong Li, Ling Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin University of Technology China School of Environmental Science and Safety Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9798400716805

The expeditious progress of information technology has encouraged the digital enhancement of the educational sector. To remedy the issues of the traditional teaching mode, a blended teaching mode of engineering drawing course has been developed with the use of smart education technology. Based on the Outcomes-based Education ( OBE ), the teaching objectives and teaching contents of the course are reconstructed by using the method of reverse design and forward implementation. By leveraging the synergy of online and offline, virtual and real, and quantitative and qualitative evaluation, a blended teaching approach that is both student-centered and teacher-guided is realized. A course knowledge graph has been built, offering fresh concepts for accomplishing personalized learning for students. The teaching practice shows that the blended teaching mode based on intelligent education has created a multi-dimensional autonomous learning space and personalized learning path for students, which effectively improves the enthusiasm and initiative of students to participate in teaching activities, and also improves the information level of teaching process and management, and promotes the organic integration of teaching and learning. © 2024 ACM.

关键词： Contrastive learning

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Reverse and inverse engineering using machine and deep learning: Futuristic opportunities and applications

Reverse and inverse engineering using machine and deep learn...

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Kumar, Sanjeev Agarwal, Pankaj Prasad, Jay Shankar Pandey, D. Chandra, Saurabh University of Petroleum Studies Dehradun India K R Mangalam University Gurugram India Greater Noida Institute of Technology Gautam Buddha Nagar India KIET Group of Institutions Ghaziabad India

ISBN: (纸本)9781032669663

This research paper investigates the use of deep and machine learning techniques in the fields of reverse and inverse engineering. Reverse engineering involves analyzing a system or product in order to understand its design or function, while inverse engineering involves creating a design or system based on a given set of requirements or specifications. We review the current state of the art in using machine and deep learning for reverse and inverse engineering, and present several case studies demonstrating the potential applications of these techniques. We also discuss the challenges and limitations of using deep and machine learning in these contexts, and outline directions for future research in this area. © 2024 The Author(s).

关键词： Deep learning

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Analysis of Attention Span of Students using Deep learning

Analysis of Attention Span of Students using Deep Learning

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2024 MIT Art, Design and Technology School of computing international conference, MITADTSoCiCon 2024

作者： Warankar, Varad Jain, Nishtha Patil, Bhavesh Faizaan, Mohammed Jagdale, Balaso Sugave, Shounak Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350362879

This research work presents an experimental work of the Analysis of Attention Span of Students using Deep learning, a novel application employing deep learning techniques for assessing student engagement in educational settings. The system incorporates facial recognition, eye tracking, and head pose analysis to offer real-time insights into students' attentiveness during *** introduction outlines the motivation behind the research, emphasizing its significance in the dynamic landscape of education. The report addresses the project's relevance in optimizing learning environments, delivering personalized education, and addressing challenges in remote learning *** implementation overview details the integration of key components and the calculation of real-time metrics related to student attention. Practical applications of the system are discussed, highlighting its role in educational adaptability, early intervention, and the generation of data-driven insights for continuous improvement in teaching methodologies. The conclusion summarizes key findings and discusses the potential implications of the Analysis of Attention Span of Students using Deep learning. This research work aims to contribute to the discourse surrounding educational technology, emphasizing adaptability and personalization for an enriched learning experience and accuracy is measured. Moreover, end user interface is implemented, and impact is analyzed. © 2024 IEEE.

关键词： Face recognition

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