This poster paper describes the authors' single-year National science Foundation (NSF) project DRL-1825007 titled, "DCL: Synthesis and Design Workshop on Digitally-Mediated Team learning" which has been ...
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This poster paper describes the authors' single-year National science Foundation (NSF) project DRL-1825007 titled, "DCL: Synthesis and Design Workshop on Digitally-Mediated Team learning" which has been conducted as one of nine awards within NSF-18-017: Principles for the Design of Digital STEM learning Environments. Beginning in September 2018, the project conducted the activities herein to deliver a three-day workshop on Digitally-Mediated Team learning (DMTL) to convene, invigorate, and task interdisciplinary science and engineering researchers, developers, and educators to coalesce the leading strategies for digital team learning. The deliverable of the workshop is a White Paper composed to identify one-year, three-year, and five-year research and practice roadmaps for highly-adaptable environments for computer-supported collaborative learning within STEM curricula. As subject to the chronology of events, highlights of the White Paper's outcomes will be showcased within the poster itself. Collaborations during this workshop identified near-term and future research directions to facilitate adaptable digital environments for highly-effective, rewarding, and scalable team-based learning. An emphasis of the workshop included the personalization of collaborations among diverse learners by automating the identification and utilization of learners' efficacies and knowledge gaps to create complementary collaborative teams that maximize avenues for peer teaching and learning. The workshop targeted the utilization and efficacy of next-generation learning architectures through a focus on instructional technologies that facilitate digitally-mediated team-based learning. These included technical objectives of: (1) identifying new research in learning analytics required to automate more optimal composition, formation, and adaptation of learner design teams;(2) detecting advances in physical and virtual learning environments that can achieve more effective and scalable observa
Visual object tracking is a challenging computer vision task with numerous real-world applications. Here we propose a simple but efficient Spectral Filter Tracking (SFT) method. To characterize rotational and translat...
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In this paper, we propose a novel tensor graph convolutional neural network (TGCNN) to conduct convolution on factorizable graphs, for which here two types of problems are focused, one is sequential dynamic graphs and...
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We present FloTree, a multi-user simulation that illustrates key dynamic processes underlying evolutionary change. Our intention is to create a informal learning environment that links micro-level evolutionary process...
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Current resume screening relies on manual review, causing delays and errors in evaluating large volumes of resumes. Lack of automation and data extraction leads to inefficiencies and potential biases. Recruiters face ...
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Current resume screening relies on manual review, causing delays and errors in evaluating large volumes of resumes. Lack of automation and data extraction leads to inefficiencies and potential biases. Recruiters face challenges in identifying qualified candidates due to oversight and time constraints. Inconsistent evaluation criteria hinder decision-making. These issues result in prolonged hiring processes, missed opportunities, and potential bias in candidate selection. The goal of this project is to develop an AI-powered Resume Analysis and Recommendation Tool, catering to the trend of recruiters spending less than 2 min on each CV. The tool will rapidly analyze all resume components while providing personalized predictions and recommendations to applicants for improving their CVs. It will present user-friendly data for recruiters, facilitating export to CSV for integration into their recruitment processes. Additionally, the tool will offer insights and analytics on popular roles and skills within the job market. Its user section will enable applicants to continually test and track their resumes, encouraging repeat usage and driving traffic. Colleges can benefit from gaining insights into students' resumes before placements. Overall, this AI-powered tool aims to enhance the resume evaluation process, benefiting both job seekers and employers. The primary aim of this project is to develop a Resume Analyzer using Python, incorporating advanced libraries such as Pyresparser, NLTK (Natural Language Toolkit), and MySQL. This automated system offers an efficient solution for parsing, analyzing, and extracting essential information from resumes. The user-friendly interface, developed using Streamlit, allows for seamless resume uploading, insightful data visualization, and analytics. The Resume Analyzer significantly streamlines the resume screening process, providing recruiters with valuable insights and enhancing their decision-making capabilities.
To remove the redundant features extracted by using 2DPCA methods, a face recognition method is presented based on 2DPCA and fuzzy-rough technique in this paper. The proposed method selects the important features for ...
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To remove the redundant features extracted by using 2DPCA methods, a face recognition method is presented based on 2DPCA and fuzzy-rough technique in this paper. The proposed method selects the important features for classification by using attribute reduction in fuzzy rough sets theory. The experimental results show the proposed method outperforms the face recognition methods based on 2DPCA.
This paper deals with a progress report on the execution of the sense making in the curriculum development for global AGILE Problem-Based learning incorporating computational thinking enhanced with ICT, which has been...
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This paper deals with a progress report on the execution of the sense making in the curriculum development for global AGILE Problem-Based learning incorporating computational thinking enhanced with ICT, which has been based on the collaborative endeavors between school of Business Management at Nanyang Polytechnic University in Singapore (NPU) and center for teaching and learning at Kansai University (KU), intending to foster the Vision 2020 skills as well as the future work skills defined by Institute of the Future. Although Problem-Based learning has been ubiquitous in the realm of the face-to-face onsite learning environment, the project is based on PBL in which project team members with common interests in entrepreneurship from both universities organize several teams to aim for startup business plans with simulation in the virtual learning environment. The paper will walk readers through the rationale behind such curriculum as well as the entire process of the curriculum development from the initial preparation to the final product including the assessment. The key factors of such curriculum development are elaborated in the conclusion.
Symmetric positive definite (SPD) matrices (e.g., covariances, graph Laplacians, etc.) are widely used to model the relationship of spatial or temporal domain. Nevertheless, SPD matrices are theoretically embedded on ...
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Cross-database non-frontal expression recognition is a very meaningful but rather difficult subject in the fields of computer vision and affect computing. In this paper, we proposed a novel transductive deep transfer ...
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Image classification plays an important role in many tasks, which is still a challenging *** paper proposes a hybrid image classification method, which integrates wavelet transform, rough set approach, and artificial ...
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Image classification plays an important role in many tasks, which is still a challenging *** paper proposes a hybrid image classification method, which integrates wavelet transform, rough set approach, and artificial neural networks (ANNs).Wavelet transform is employed to decompose the original images into different frequency sub-bands, then a set of statistical features are extracted from the wavelet coefficients, the feature set can be viewed as an information *** wavelet transform well decorrelates images, there still exist dependencies between *** the features extracted from the coefficients may be *** the features from one sub-band are dependent on the features from another sub-band, the later one can be *** set approach is utilized to remove the correlated or redundant *** reduced information system finally fed into neural network for *** performance of the method is evaluated in terms of training accuracy and testing accuracy, the experimental results confirm the effectiveness of the proposed approach.
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