The construction of new engineering disciplines and the "Double Tops" initiative have put forward new requirements for the training of engineering professionals. Taking the course of C language programming a...
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The development of technology has changed human life and, at the same time, education and teaching methods. The development and application of technology in education are explained one by one. In the face of the emerg...
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To meet the needs of students for personalized learning during the process of network teaching, a personalized recommendation scheme based on combined filtering is proposed. Firstly, based on the analysis of deep lear...
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This study investigates using machine learning (ML), the Internet of Things (IoT), and cloud computing to predict cardiovascular diseases. Integrating these advanced technologies in healthcare enables the collection, ...
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This study compared the effects of digital storytelling-enhanced scenario-based learning with traditional scenario-based learning on students' intrinsic motivation, learning performance, and behavioral engagement....
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ISBN:
(纸本)9781665453318
This study compared the effects of digital storytelling-enhanced scenario-based learning with traditional scenario-based learning on students' intrinsic motivation, learning performance, and behavioral engagement. We conducted a mixed-methods quasi-experiment to test the two conditions (N = 37 for digital storytelling-enhanced scenario- based learning and N = 33 for traditional scenario-based learning). The quantitative results indicated that digital storytelling-enhanced scenario-based learning led to significantly higher intrinsic motivation, learning performance, and sustained engagement in task completion compared to the traditional scenario-based learning setting. The qualitative results showed that students in the digital storytelling group enjoyed the new learning mode, perceived a strong self-identification and connection with the fictitious character, and appreciated the digital story visual design. Students in the two groups all agreed that authentic scenario-based learning tasks helped them better apply and reflect on the knowledge learned.
Evolutionary computation for addressing high-dimensional expensive problems (HEPs) characterized by both high-dimensional decision variables and resource-intensive evaluations is an important area. In this study, we i...
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ISBN:
(纸本)9789819771837;9789819771844
Evolutionary computation for addressing high-dimensional expensive problems (HEPs) characterized by both high-dimensional decision variables and resource-intensive evaluations is an important area. In this study, we introduce a novel approach, namely the Hierarchical Diffusion teaching-learning-based Optimizer with Variational autoencoder (HDTOV). Firstly, we employ a variational autoencoder to reduce problem dimensions and facilitate the learning of the optimization process. Secondly, we employ a hierarchical population reconstruction strategy to enhance population diversity. Lastly, to exploit the population more effectively, we implement a diffusion mechanism to prevent premature convergence. The proposed method is validated through experiments on a real-life optimization problem arising from the operation of mobile edge computing systems. The experimental results demonstrate the efficacy and efficiency of HDTOV in addressing HEPs by its outperforming the state of the art.
In order to meet the learning needs of students in the "Internet plus" era, this research has constructed a teaching model integrating multi-dimensional interaction and collaborative learning, and optimized ...
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The financial market is inherently complicated and dynamic, which has increased interest in adapting machine learning (ML) approaches to stock market forecasting. The body of research on machine learning-based stock m...
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Traffic engineering (TE) mechanisms are crucial for achieving optimal levels of network performance over wide-area networks across geographically distributed datacenters. Existing work on traffic engineering formulate...
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ISBN:
(纸本)9798350386066;9798350386059
Traffic engineering (TE) mechanisms are crucial for achieving optimal levels of network performance over wide-area networks across geographically distributed datacenters. Existing work on traffic engineering formulated the challenges at hand as combinatorial optimization problems, which could take hours to compute on modern wide-area network topologies at the scale of thousands of nodes. To improve the performance of TE mechanisms, we introduce DeepTE, a new TE framework based on machine learning (ML) that is designed for the best possible scalability and performance, capable of completing the computation within milliseconds with networks involving thousands of nodes, and of generating near-optimal TE policies while guaranteeing that all constraints are satisfied. DeepTE is also designed with a distributed ML model architecture, which can be horizontally scaled up to multiple GPUs for even better performance. With real-world traffic matrices, our extensive array of performance evaluations of DeepTE on various network topologies and TE problems show that DeepTE is capable of producing policies within 5% of the optimal results while offering up to 100x performance improvements over state-of-the-art traffic engineering mechanisms.
This paper introduces a novel system for multilingual education, leveraging Optical Character Recognition (OCR) and Artificial Intelligence (AI). The system aims to democratize access to educational resources by extra...
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