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Integrating computational thinking practices into early childhood education in culturally responsive ways: Insights from research–practice partnership

Future in Educational Research

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Future in Educational Research 2024年第4期2卷 359-381页

作者： Jeanne Hubelbank Mia Dubosarsky Shakhnoza Kayumova Tiffany Davis Nea Sann Shawn Fortin Gillian Smith Independent Evaluator Sudbury Massachusetts USA The STEM Education Center Worcester Polytechnic Institute Worcester Massachusetts USA Department of STEM Education and Teacher Development University of Massachusetts Dartmouth Massachusetts USA Ashburnham Westminster Regional School District Ashburnham Massachusetts USA Worcester Child Development Head Start Worcester Massachusetts USA Teaching & Learning Belchertown Public Schools Belchertown Massachusetts USA Interactive Media & Game Development Department of Computer Science Worcester Polytechnic Institute Worcester Massachusetts USA

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.

关键词： computational thinking cultural responsive practices early childhood research practice partnership teacher professional development

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Confronting autism in urban bangladesh: Unpacking infrastructural and cultural challenges

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EAI Endorsed Transactions on Pervasive Health and Technology 2018年第14期4卷

作者： Ehsan, Upol Sakib, Nazmus Haque, Md Munirul Soron, Tanjir Saxena, Devansh Ahamed, Sheikh Iqbal Schwichtenberg, Amy Rabbani, Golam Akter, Shaheen Alam, Faruq Begum, Azima Ahmed, Syed Ishtiaque School of Interactive Computing Georgia Institute of Technology AtlantaGA30332 United States Department of MSCS Marquette University MilwaukeeWI53233 United States Regenstrief Center for Healthcare Engineering Purdue University West LafayetteIN United States Neurodevelopmental Disability Protection Trust Bangladesh Institute of Pediatric Neurodisorder and Autism Bangladesh National Institute of Mental Health Dhaka Bangladesh Dhaka Medical College and Hospital Dhaka Bangladesh Department of Computer Science University of Toronto Toronto Canada

Autism Spectrum Disorder (ASD) is a critical problem worldwide;however, low and middle-income countries (LMICs) often suffer more from it due to the lack of contextual research and effective care infrastructure. Moreover, ASD in LMICs offers unique challenges as cultural misperceptions and social practices often impede effective care there. However, the recent growth in mobile phone adoption in many LMICs has created a timely opportunity for improving ASD care practices through digital means. We present qualitative findings on the challenges of designing mobile assistive technologies for ASD in Bangladesh. Our data reveals a set of technical, social, and cultural challenges with caregivers and care professionals for designing a meaningful technology for them. We show how those challenges are deeply rooted in the broader infrastructural and cultural tensions of the country. We also present embedded design implications to overcome these challenges. © 2018 Upol Ehsan et al., licensed to EAI.

关键词： mHealth

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Navigating the Artificial Intelligence Revolution in NeuroOncology: A Multidisciplinary Viewpoint

SSRN

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SSRN 2024年

作者： Saxena, Sanjay Panda, Soumyaranjan Tiwari, Ekta Singh, Rajesh Fouda, Mostafa M. Kalra, Mannudeep K. Kotecha, Ketan Saba, Luca Suri, Jasjit S. Department of Computer Science & Engineering International Institute of Information Technology Odisha Bhubaneswar India Department of Computer Science & Information Technology Mahatma Gandhi Central University Bihar India Department of Computer & Engineering Visvesvaraya National Institute of Technology Maharashtra Nagpur India Department of Research and Innovation Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Department of Electrical and Computer Engineering Idaho State University PocatelloID83209 United States Department of Radiology Massachusetts General Hospital BostonMA United States Symbiosis centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Cagliari Italy Department of ECE Idaho State University PocatelloID83209 United States Department of CE Graphics Era Deemed to be University Dehradun248002 India University Center for Research & Development Chandigarh University Mohali India Pune India Stroke Diagnostic and Monitoring Division AtheroPoint RosevilleCA95661 United States Harvard Medical School United States University Hospital of Cagliari Italy

This comprehensive review presents the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in neuro-oncology, focussing their groundbreaking impact on the field. It gives a historical overview of AI's evolution in area of neuro-oncology, representing its progress from basic data management to high-level predictive analytics that now significantly makes personalized treatment ***, This study examines various ML based methods that emphasize brain tumor classification, genetic profiling, and individualized care plans proposals, which basically reflects the shift towards precision medicine. This paper also highlights, developments in DL particularly in the development of neuro-imaging analysis, which has significantly guided surgical planning and radiation therapy for improved patient outcomes. Through various case studies, the paper presents recent breakthroughs where AI interventions have noticeably enhanced the treatment of neuro-oncological conditions. Moreover, It also addresses the ethical considerations encompassing the use of automated decision-making tools, emphasizing the need for ethical governance in AI deployment. By integrating multidisciplinary perspectives, the study underscores the collaborative effort between clinicians, data scientists, and ethicists, which is very crucial for the ethical and effective implementation of AI in current clinical practice. The main aim of discussion is to equip neurooncologists/sergeons and doctors with understandings into sustainable AI adoption which ensures that AI's path in neuro-oncology aligns with both clinical needs for personalized medicine and ethical *** paper concludes by outlining considered methods and frameworks for the future integration of AI in neuro-oncology, predicting trends, and preparing to meet the challenges ahead. © 2024, The Authors. All rights reserved.

关键词： Personalized medicine

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 IEEE.

关键词： Image enhancement

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CAD CAM MANUFACTURING OF PROTOTYPE SHIPBOARD COMPONENTS

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NAVAL ENGINEERS JOURNAL 1990年第3期102卷 93-99页

作者： CALVERT, TE RODRIGUEZ, FA SLEBZAK, JS Thomas E. Calvert P.E.: is a senior project engineer with the Propulsion and Auxiliary Systems Department David Taylor Research Center Annapolis Md. His interests include application of computers to all aspects of engineering with particular emphasis on utilization of small computers. Mr. Calvert is a licensed professional engineer in Maryland. He received a BSEE from Drexel University in 1969 and since that time has completed a number of graduate courses related to machinery acoustics. Francisco A. Rodriguez:is an engineer with the Propulsion and Auxiliary Systems Department David Taylor Research Center. He was formerly with the Computer-Aided Design/Interactive Graphics Group of the Division of Engineering and Weapons U.S. Naval Academy. His interests include interfacing the computer aided design to the computer aided manufacturing along with related software and hardware development. Mr. Rodriguez received a BSEEfrom the University of Virginia in 1968. James S. Slebzak:is a mechanical engineering technician with the Propulsion and Auxiliary Systems Department David Taylor Research Center. He received his machinists papers in 1971 after serving his apprenticeship at David Taylor Research Center. He continued his education and became the senior numerical control programmer at the Annapolis Laboratory. He completed his mechanical technology degree from Anne Arundel Community College in 1986. His interests are in the application of numerically controlled manufacturing techniques to prototype machinery components.

The machine shop at the Annapolis Laboratory of the David Taylor Research center (DTRC) provides model making and prototype support to a large variety of naval ship related engineering projects. In order to meet these challenging requirements, computer aided design/computer aided manufacturing (CAD/CAM) techniques are being used to produce “one of a kind” prototypes or very low volume production parts. The use of computer aides in these cases is to facilitate the accurate manufacture of a difficult part, rather than to improve manufacturing efficiencies. In addition, the approach provides the flexibility required to support research and development projects. Several examples of prototype shipboard components manufactured using CAD/CAM techniques are presented in this paper. The hardware and software that facilitated these projects are discussed. The examples described have met the requirements to produce a wide variety of prototype shipboard machinery components quickly and accurately.

关键词： Ship Equipment

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Correction to “The Open Catalyst 2020 (OC20) Dataset and Community Challenges”

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ACS Catalysis 2021年第21期11卷 13062-13065页

作者： Lowik Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi Morgane Riviere Kevin Tran Javier Heras-Domingo Caleb Ho Weihua Hu Aini Palizhati Anuroop Sriram Brandon Wood Junwoong Yoon Devi Parikh C. Lawrence Zitnick Zachary Ulissi Facebook AI Research (FAIR) 1 Hacker Way Menlo Park California 94025 United States Department of Chemical Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Computer Science Department Stanford University Stanford California 94305 United States National Energy Research Scientific Computing Center (NERSC) Lawrence Berkeley National Laboratory Berkeley California 94720 United States School of Interactive Computing Georgia Tech Atlanta Georgia 30332 United States Scott Institute for Energy Innovation Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States

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THE COMMAND SUPPORT AT-SEA EXPERIMENT

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NAVAL ENGINEERS JOURNAL 1990年第3期102卷 25-36页

作者： BUSCHER, DJ SUNDAY, DM David J. Buscher:is an electrical engineer on the Principal Professional Staff of the Fleet Systems Department of The Johns Hopkins University Applied Physics Laboratory. He earned his BS degree in electrical engineering from Villanova University in 1967 an MSEE degree from the University of Maryland in 1972 and an MS degree in technical management from The Johns Hopkins University in 1985. He was the lead engineer on the Command Support At-Sea Experiment Project. He started his career in 1967 with the U.S. Army Harry Diamond Laboratory working on computer and communications projects. During this time he worked in Vietnam with the United States Army Vietnam Science Advisor Office. Since joining JHU/APL in 1978 he has worked on a variety of projects associated with computer graphics hospital patient information systems computer networks autonomous underwater vehicles and satellite communications. He is currently assistant group supervisor of the Computer Systems Group. Daniel M. Sunday:is a mathematician on the Principal Professional Staff of the Fleet Systems Department of The Johns Hopkins University Applied Physics Laboratory. He earned his Ph.D. in mathematics from the University of Minnesota in 1971 and did postdoctoral research on neural networks at the University of California at Berkeley. Since joining JHU/APL in 1980 Dr. Sunday has worked as a designer and developer of software systems with emphasis on medical imaging interactive graphics natural language interfaces and software standards. He was the network designer for the Headquarters Integrated Office System (HIOS) Network for the Army at the Pentagon. He is currently the lead software engineer on the Command Support At-Sea Experiment Project.

The Command Support At-Sea Experiment (CS@SE) provides experimental advanced graphics display systems consisting of large screen color displays and operator console color displays in the combat information center (CIC) of an Aegis cruiser and in the tactical flag command center (TFCC) of an aircraft carrier. CS@SE systems are designed to prototype potential command support capabilities in an at-sea environment to validate and refine requirements for planned production system upgrades. These systems use sophisticated color graphics techniques to provide real-time tactical displays that improve the availability of information to an operator by reducing clutter through the use of color, area fill, transparen overlays and intensity coding of track symbols. Interfaces wen developed with the Aegis Display System (ADS), Shipboarc Gridlock System with Auto-correlation (SGS/AC), Flag Dats Display System (FDDS) and Tomahawk Engagement Planning and Exercise Evaluation System (TEPEE) that provided the data for the presentation of a tactical display. Display elements included both real-time and over-the-horizon (OTH) surface track data, velocity leaders, tags, uncertainty ellipses, and history trails. The display also included filled land masses, country boundaries, commercial airways, cities, graphics overlays (i.e., operational areas), weapon system missile performance contours, and engagement plans. This paper describes the experiment, its installation and integration into the shipboard environments of an Aegis cruiser (USS Leyte Gulf ) and an aircraft carrier (USS America , its usage by the ships companies and embarked staffs, and the experiment result! and findings. Key conclusions of the experiment are: 1 Advanced graphics color displays can significantly enhance the ability of the warfighter to assimilate a complex tactical display. 2 Both ships reported a requirement for a correlated OTH and real-time track display with the ability to clearly differentiate the two types

关键词： computer graphics

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Boundary Fractal Analysis of Two Cube-oriented Grains in Partly Recrystallized Copper

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IOP Conference Series: Materials science and Engineering 2015年第1期82卷

作者： J Sun Y B Zhang A B Dahl K Conradsen D Juul Jensen Danish-Chinese Center for Nanometals Section for Materials Science and Advanced Characterization Department of Wind Energy Technical University of Denmark Risø Campus 4000 Roskilde Denmark Section for Image Analysis and Computer Graphics Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads 2800 Kgs. Lyngby Denmark

The protrusions and retrusions observed on the recrystallizing boundaries affect the migration kinetics during recrystallization. Characterization of the boundary roughness is necessary in order to evaluate their effects. This roughness has a structure that can be characterized by fractal analysis, and in this study the so-called 'Minkowski sausage' method is adopted. Hereby, two cube-oriented grains in partly recrystallized microstructures are analyzed and quantitative information regarding the dimensions of protrusions/retrusions is obtained.

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Characterization of boundary roughness of two cube grains in partly recrystallized copper

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IOP Conference Series: Materials science and Engineering 2015年第1期89卷

Protrusions and retrusions typically form on recrystallizing boundaries and thus the boundaries often appear rough. Characterization of the boundary roughness is necessary in order to evaluate the effects of protrusions and retrusions on boundary migration. In the current work, a variable termed area integral invariant is employed to provide quantitative information of individual protrusions/retrusions on boundaries surrounding two selected recrystallizing grains in partly recrystallized copper as well as of the overall roughness of the boundaries.

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