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Learning from the COVID-19 pandemic: Improving academic continuity in workforce development programs

New Directions for Community Colleges 2024年第205期2024卷 143-151页

作者： Katie Shakour Tim Ransom Eliza Gallagher Karen Johnson Rebecca Short PhD Jonathan Beck Kapil Chalil Madathil Center for Workforce Development Clemson University Clemson South Carolina USA Department of Engineering and Science Education College of Engineering Computing and Applied Sciences Clemson University Clemson South Carolina USA School of Aviation Southern Illinois University Carbondale Illinois USA Rebecca Short Clemson University Center for Workforce Development College of Engineering Computing and Applied Sciences 1 Research Drive Suite 400 Greenville SC 29607. National Center for Autonomous Technologies Northland Community and Technical College Thief River Falls Minnesota USA Departments of Civil and Industrial Engineering Clemson University College of Engineering Computing and Applied Sciences Clemson University Clemson South Carolina USA

The COVID-19 pandemic caused an abrupt change in educational programs worldwide, including workforce development education in community colleges. Given the hands-on requirements of these programs, considerations for changes included if and how instructors and students could maintain academic continuity during the pandemic. This article focuses on aviation maintenance technology schools (AMTS) as a case study to understand how programs that rely heavily on hands-on learning responded to COVID-19 significant disruption to education. The Federal Aviation Administration (FAA) must approve educational training for aviation maintenance careers, and the FAA requires specific hands-on activities in the curriculum. Of the 182 AMTS in the United States, 143 are located within community colleges. We conducted 43 interviews with AMTS students, administrators, and instructors from 18 different community colleges. Following content analysis of the interviews, the authors identified six findings related to how these programs responded to the pandemic, with special attention to maintaining academic stability. The article advocates for integrating digital learning tools (DLT) to create resilient educational programs when disruptions occur. These tools allow for students to continue to asynchronously practice the procedures and familiarize themselves with the materials needed for projects, provide students immediate feedback on their learning, and save schools money on expensive resources when students require extra practice on certain skills and processes. The application of these tools is relevant beyond the pandemic, helping students in many scenarios succeed in the face of natural disasters, family obligations, and the need for extra learning resources.

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A Landscape-Aware Differential Evolution for Multimodal Optimization Problems

arXiv

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arXiv 2024年

作者： Lin, Guo-Yun Chen, Zong-Gan Liu, Chuanbin Jiang, Yuncheng Kwong, Sam Zhang, Jun Zhan, Zhi-Hui School of Computer Science South China Normal University Guangzhou510631 China The Center for Scientific Research and Development in Higher Education Institutes Ministry of Education China The Department of Computing and Decision Science Lingnan University Hong Kong The College of Artificial Intelligence Nankai University Tianjin300350 China The Hanyang University ERICA 15588 Korea Republic of

How to simultaneously locate multiple global peaks and achieve certain accuracy on the found peaks are two key challenges in solving multimodal optimization problems (MMOPs). In this paper, a landscape-aware differential evolution (LADE) algorithm is proposed for MMOPs, which utilizes landscape knowledge to maintain sufficient diversity and provide efficient search guidance. In detail, the landscape knowledge is efficiently utilized in the following three aspects. First, a landscape-aware peak exploration helps each individual evolve adaptively to locate a peak and simulates the regions of the found peaks according to search history to avoid an individual locating a found peak. Second, a landscape-aware peak distinction distinguishes whether an individual locates a new global peak, a new local peak, or a found peak. Accuracy refinement can thus only be conducted on the global peaks to enhance the search efficiency. Third, a landscape-aware reinitialization specifies the initial position of an individual adaptively according to the distribution of the found peaks, which helps explore more peaks. The experiments are conducted on 20 widely-used benchmark MMOPs. Experimental results show that LADE obtains generally better or competitive performance compared with seven well-performed algorithms proposed recently and four winner algorithms in the IEEE CEC competitions for multimodal optimization. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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Text Information Mining in Cyberspace: An Information Extraction Method Based on T5 and KeyBERT

Text Information Mining in Cyberspace: An Information Extrac...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Kuan Liu Yumei Li Yuanhua Qi Ning Qi Mengran Zhai Information Research Institute of Shandong Academy of Sciences Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Institute of Economy and Informatization Development Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Shandong Institute of Economy and Informatization Development Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the development of the digital economy and the advent of the big data era, the rapid growth of textual information in cyberspace has placed higher demands on information processing technology. This paper proposes an information extraction technique based on the Text-to-Text Transfer Transformer, known as T5, and KeyBERT models, aiming to efficiently distill the key content from textual information in cyberspace. This method combines automatic summarization and keyword extraction to form information briefs. The experiments selected representative policy documents related to data elements as input data and employed information entropy and ROUGE scores as metrics to evaluate the automatic summarization model. The results were compared with the outputs of similar models. Experimental results indicate that the T5 model outperforms other models in terms of summarization effectiveness, and the proposed information extraction method shows significant advantages in readability and processing efficiency, demonstrating practical application value.

关键词： Measurement Biological system modeling Cyberspace Text summarization Information processing Information retrieval Transformers Data models Data mining Information entropy

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Lane marking detection algorithm based on high-precision map and multisensor fusion

Lane marking detection algorithm based on high-precision map...

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作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Huang, Guangyan Wang, Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Software Nanjing Institute of Industry Technology Nanjing China Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department Nanjing Institute of Industry Technology Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China School of Information Technology Deakin University Melbourne Australia

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. In the process of generating high-precision maps or in the uncovered areas of high-precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © 2020 John Wiley & Sons, Ltd.

关键词： Roads and streets

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN Computer Science 2024年第7期5卷 1-20页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Design of an Energy Efficient Spin Transistor Assisted Toggle Spin Torque Mram Bit Cell

SSRN

引用

SSRN 2023年

作者： Jain, Divyansh Dahiya, Sunil Department of VLSI Design Center for Development of Advanced Computing Punjab Mohali160071 India Department of Intellectual Property Rights Lovely Professional University Punjab Phagwara144001 India Department of Physical Sciences Indian Institute of Science Education and Research Punjab Mohali140306 India

Spin-based non-volatile memory bit cells have been proposed for applications ranging from logic-in-memory computing to quantum computing. This paper deals with the toggle spin torque magnetic random-access memory (TST-MRAM) bit cell by micromagnetic simulations. The concept of voltage-controlled magnetic anisotropy (VCMA) is utilized to model and simulate the designed cells. In this paper, the TST magnetization precession is studied in a nanopillar spin-valve. The magnetization switching is considered under an external magnetic field, and it is simulated with the micromagnetic simulation (MMS) toolkit to evaluate the designed TST-MRAM bit cell parameters. The present work utilizes different material interfaces (i.e., HM-CoFeB-NM-MgO) to minimize the switching error rates during the MRAM design process. The interfacial perpendicular magnetic anisotropy-based TST-MRAM bit cell obtains less polarization factor, more anisotropic constant, and less saturated magnetization than classical magnetic RAM models. The factors related to the designed bit cell are explained theoretically and graphically in this work. Finally, the parameters have been evaluated for the proposed TST-MRAM bit cell and will take part in the design process. © 2023, The Authors. All rights reserved.

关键词： Magnesia

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Quantum computing in Transport Science: A Review

arXiv

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arXiv 2025年

作者： Niu, Chence Irannezhad, Elnaz Myers, Casey Dixit, Vinayak Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education School of Ecology Environment and Resources Guangdong University of Technology Guangzhou510006 China School of Civil and Environmental Engineering UNSW Sydney SydneyNSW2052 Australia School of Physics Mathematics and Computing UWA 35 Stirling Hwy Crawley WA 6009 United Kingdom

Quantum computing, leveraging the principles of quantum mechanics, has been found to significantly enhance computational capabilities in principle, in some cases beyond classical computing limits. This paper explores quantum computing's potential to address complex, large-scale problems in transportation systems. It focuses on three principal paradigms: Gate-based quantum computing, Quantum annealing, and Quantum machine learning, which, though based on gate-based quantum computing, is treated as distinct due to its unique methods and applications. Each paradigm's foundational concepts, practical applications, and potential impacts on the field are discussed to provide a comprehensive overview of quantum computing strategies and their future implications. © 2025, CC BY-SA.

关键词： Quantum computers

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Identifying average causal effect in regression discontinuity design with auxiliary data

arXiv

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arXiv 2024年

作者： Feng, Xinqin Hu, Wenjie Yang, Pu Li, Tingyu Zhou, Xiao-Hua Department of Probability and Statistics Peking University China Perelman School of Medicine University of Pennsylvania United States Department of Information and Computing Science Peking University China Chongqing Key Laboratory of Child Neurodevelopmental and Cognitive Disorders Children’s Hospital of Chongqing Medical University Ministry of Education Key Laboratory of Child Development and Disorders National Clinical Research Center for Child Health and Disorders China Beijing International Center for Mathematical Research Department of Biostatistics Peking University China

Regression discontinuity designs are widely used when treatment assignment is determined by whether a running variable exceeds a predefined threshold. However, most research focuses on estimating local causal effects at the threshold, leaving the challenge of identifying treatment effects away from the cutoff largely unaddressed. The primary difficulty in this context is that the treatment assignment is deterministically defined by the running variable, violating the commonly assumed positivity assumption. In this paper, we introduce a novel framework for identifying the average causal effect in regression discontinuity designs. Our approach assumes the existence of an auxiliary variable for which the running variable can be seen as a surrogate, and an additional dataset that consists of the running variable and the auxiliary variable alongside the traditional regression discontinuity design setup. Under this framework, we propose three estimation methods for the ATE, which resembles the outcome regression, inverse propensity weighted and doubly robust estimators in classical causal inference literature. Asymptotically valid inference procedures are also provided. To demonstrate the practical application of our method, simulations are conducted to show the good performance of our methods;besides, we use the proposed methods to assess the causal effects of vitamin A supplementation on the severity of autism spectrum disorders in children, where a positive effect is found but with no statistical significance. Copyright © 2024, The Authors. All rights reserved.

关键词： Data fusion

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Assessing the Impact of IoT Enabled E-Learning System for Higher education

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SN Computer Science 2023年第5期4卷 1-9页

作者： Haque, Shameemul Haque, Md Alimul Kumar, Devanshu Mishra, Khushboo Islam, Farheen Ahmad, Sultan Kumar, Kailash Mishra, Binay Kumar Al-Hafeez College Ara India Department of Computer Science Veer Kunwar Singh University Ara 802301 India Department of Physics Veer Kunwar Singh University Ara 802301 India Department of Education Patna Women’s College Patna India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

In this paper, a hypothetical investigation alongside a genuine review is investigated. The theoretical and empirical results demonstrated the significant impact of IoT in the physical training system in terms of learning and making due factors. Now and again, like the hyper-availability, joint effort, and exploration of potential open doors, the impact is critical. Eminent advancements, like, the IoT at present, are quickly created in the computerized world and changing the conventional school system into a more versatile, adaptable, and more productive E-learning system, where the enormous number of physical and virtual collaborating objects are engaged with the most common way of learning. IoT is a somewhat developing innovation that works with communication between genuine and virtual items. As it creates and designs, it fills in scale and aspects that impact the setting of our lives, such as education, particularly advanced education;this article focuses on examining the anticipated impact of IoT in unrivaled training. It is anticipated that the Internet of Things will enable the development of intelligent E-learning frameworks capable of anticipating and deciding the specific instructional needs of understudies based on data analysis from related data. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AI Distance learning education Higher education IoT Learning platform

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research on the Distribution Characteristics of Standardized Big Data Resources Based on Data Visualization——Taking Standardization Practice in Shandong Province as an Example

Research on the Distribution Characteristics of Standardized...

引用

IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Jinyang Sun Dongdong Peng Zhe Lu Mengran Zhai Ning Qi Yuanhua Qi SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Jinan Municipal Bureau of Big Data Jinan China Shandong Institute of Economy and Informatization Development Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology Shandong Academy of Sciences Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Standardized big data, as a product of the combination of big data and standardization work, objectively reflects the status quo and trends of standardization work. An in-depth study of the distribution characteristics of standardized big data resources has important value for improving the efficiency of standardization. Therefore, this paper explores the characteristics of the distribution of standardization resources from the dimensions of spatial and temporal distribution, industry distribution, standard type distribution, and distribution of think tank experts through visual analysis methods. The study shows that: (1) the distribution of standardization resources in Shandong Province presents significant hierarchical and geographical characteristics; (2) from the viewpoint of the distribution of industry types and standard types, the distribution of standardization resources in Shandong Province presents a typical "long-tailed distribution"; (3) the distribution of think-tank experts presents obvious "agglomeration effect".

关键词： Industries Technological innovation Heavily-tailed distribution Animals Urban areas Data visualization Forestry Big Data Fisheries Standards

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