Interdisciplinary knowledge sharing is a crucial component of higher education. The use of asynchronous online discussion forums as a medium for fostering interdisciplinary knowledge sharing is effective, as it allows...
Interdisciplinary knowledge sharing is a crucial component of higher education. The use of asynchronous online discussion forums as a medium for fostering interdisciplinary knowledge sharing is effective, as it allows for the sharing, posting, and reflecting of information among learners outside of traditional classroom settings. However, the sheer volume of posts in such forums can threaten the quality of discussion and the ability of instructors to provide timely evaluations. To address this issue, an automated post-rating system has been developed utilizing a BERT-based AI model. This system evaluates learners' posts and provides prompt categorization outcomes into three categories: non-informative, informative, and neutral, within 10 seconds. Our model demonstrated appropriate accuracy in assessing the information density in forum posts, indicating potential benefits for both learners and instructors. Specifically, it achieved 67%, 68%, and 75% accuracy rates for posts categorized as discussion, comment, and reply, respectively. To assess the effectiveness of the system, it was tested and evaluated using two courses, “Python Programming” and “Introduction to AI,” through the use of a questionnaire. Results revealed that learners held positive evaluations of the system, noting improvements in post quality, reduced plagiarism, and enhanced comprehension. Additionally, feedback from open-ended questions also indicated the benefits of automatic feedback on post quality.
Cross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problem in micro-expression analysis. CDMER is more challenging than the conventional micro-expression recognition (MER),...
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Creativity is one of the most crucial skills for success in life in the 21st century. However, assessing creativity in an automated, objective way is challenging. In this study, we designed and validated an automated ...
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XiangShan, a rising star of RISC-V-based processors proposed by Chinese Academy of science, is famous for its high performance and agile methodology. At present, the second-generation Yanqi Lake architecture of XiangS...
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ISBN:
(纸本)9781665460583
XiangShan, a rising star of RISC-V-based processors proposed by Chinese Academy of science, is famous for its high performance and agile methodology. At present, the second-generation Yanqi Lake architecture of XiangShan has already reached the frequency of 2GHz at the 14nm process node, which ranks the first in the field of open-source RISC-V-based processors. One of the key approaches that contribute to its high performance is prefetch. A timely and accurate prefetcher can effectively eliminate cache misses and thus hide memory access latency. Aiming at the specific environment of XiangShan, out-of-order and parallel, a stride-based prefetcher for the L1DCache is achieved on the Yanqi Lake structure of XiangShan. Serious benchmarks from SPEC2006 were run on XiangShan, the result of which demonstrates that our prefetcher improves the performance of XiangShan by an average of 2.5% and up to 13.3%.
In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a co...
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In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches.
作者：
Cui, ZhenXu, ChunyanZheng, WenmingYang, JianPCA Lab
Key Lab of Intelligent Perception and Systems High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Child Development and Learning Science
Ministry of Education Research Center for Learning Science Southeast University Nanjing210096 China
Visual relationship detection can bridge the gap between computer vision and natural language for scene understanding of images. Different from pure object recognition tasks, the relation triplets of subject-predicate...
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The study examines the research–practice partnership (RPP) model aimed at co-constructing and integrating computational thinking (CT) in culturally responsive (CR) ways within early childhood and elementary (PreK-5) ...
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The study examines the research–practice partnership (RPP) model aimed at co-constructing and integrating computational thinking (CT) in culturally responsive (CR) ways within early childhood and elementary (PreK-5) classrooms, focusing on teachers' RPP experiences. Recent research underlines the importance of integrating CT in early childhood and elementary education, demonstrating that culturally, linguistically, and developmentally responsive computational activities in PreK-5 settings are key to such efforts. However, there is a gap in research regarding effective professional development models that would prepare early childhood teachers in terms of both disciplinary ideas and practices of CT, as well as attending to cultural, linguistic, and developmental differences in young children in asset-based ways. Our research contributes to this body of knowledge by examining how RPP can be a model for teacher learning in which teachers, administrators, and researchers can co-develop knowledge and confidence in integrating CT into the PreK-5 teaching curriculum and practices in CR ways. The study outlines the collaborative development of processes and frameworks co-constructed by the RPP educators, as well as teacher-developed curricular materials and lesson plans integrating CT in CR ways. Findings include teachers' experience of these lessons and teacher insights about the support needed to incorporate CT and CR practices into their existing curricula. The study concludes by highlighting the potential challenges and opportunities inherent in such endeavors, thereby contributing to the broader discourse on supporting CT integration into early childhood classrooms in CR ways.
Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clin...
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3D virtual reality, including the current generation of multi-user virtual worlds, has had a long history of use in education and training, and it experienced a surge of renewed interest with the advent of Second Life...
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Abstract: In this comprehensive investigation, we evaluate the efficacy of the Fenton process in degrading basic fuchsin (BF), a resistant dye. Our primary focus is on the utilization of readily available, environment...
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