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作者： Flamer, E Adusei-Poku, A DELOITTE CONSULTING LLP ARLINGTON VA JOINT ADVANCED DISTRIBUTED LEARNING CO LAB (JADLL) ORLANDO FL Defence Support Services Center Advanced Distributed Learning Initiative Orlando FL

The scope of the ELRR Prototype Systems Integration Plan (SIP) is to provide technical guidance for integrating ELRR Prototype with ADL TLA sandbox components, such as the Authoritative Learner Record Store (LRS), Competency and Skills System (CaSS), and Enterprise Course Catalog (ECC), along with a recommended resource mixture to conduct the integration activities. The intended audiences for this document are developers or engineers who are familiar with TLA standards and core cloud computing principles.

关键词： Operating systems Computer programs Governments Specifications Department of defense Distance learning Information operations Markup languages Plastic explosives Political science Public administration Social sciences Standards Authentication Computers Language Launching

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Federated learning for Inference at Anytime and Anywhere

arXiv

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arXiv 2022年

作者： Liu, Zicheng Li, Da Fernandez-Marques, Javier Laskaridis, Stefanos Gao, Yan Dudziak, Lukasz Li, Stan Z. Hu, Shell Xu Hospedales, Timothy School of Information Science & Electronic Engineering Zhejiang University Hangzhou China Machine Learning & Data Intelligence Samsung AI Center Cambridge United Kingdom Automated AI Samsung AI Center Cambridge United Kingdom Distributed AI Samsung AI Center Cambridge United Kingdom Department of Computer Science and Technology The University of Cambridge Cambridge United Kingdom School of Engineering Westlake University Hangzhou China School of Informatics The University of Edinburgh Edinburgh United Kingdom

Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities. However, there is no unified framework that addresses all these problems together. This paper studies the challenges and opportunities of exploiting pre-trained Transformer models in FL. In particular, we propose to efficiently adapt such pre-trained models by injecting a novel attention-based adapter module at each transformer block that both modulates the forward pass and makes an early prediction. Training only the lightweight adapter by FL leads to fast and communication-efficient learning even in the presence of heterogeneous data and devices. Extensive experiments on standard FL benchmarks, including CIFAR-100, FEMNIST and SpeechCommandsv2 demonstrate that this simple framework provides fast and accurate FL while supporting heterogenous device capabilities, efficient personalization, and scalable-cost anytime inference. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Blueshift: Rebalancing engineering engagement, integrity, and learning outcomes across an electronically-enabled remediation hierarchy

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Computers in Education Journal 2019年第1期10卷 1-12页

作者： DeMara, Ronald F. Sheikhfaal, Shadi Wilder, Paul J. Chen, Baiyun Hartshorne, Richard Department of Electrical Engineering and Computer Science University of Central Florida OrlandoFL32816 United States Department of Engineering Computer Program and Technology Valencia College OrlandoFL32811 United States Center for Distributed Learning University of Central Florida OrlandoFL32816 United States Division Dean for Learning Sciences Educational Research University of Central Florida OrlandoFL32816 United States

With a growing number of enrollments in engineering programs, more time and energy for tutoring and grading exams/quizzes is necessary. The BLUESHIFT framework utilizes a synergistic combination of faculty, Graduate Teaching Assistants, and educational technologies to provide reliable approaches to not only meet the increased time, cost, and energy efficiency needed for increasing numbers of engineering students, but also to focus on improving the quality of education, skills, and employability of our graduates in computing-related fields. An Evaluation and Proficiency center (EPC) has been piloted at a large state university, where key elements of the BLUESHIFT learning flow have been integrated into selected undergraduate engineering courses. The EPC integrates computer-based evaluation with a close-knit review and learning cycle based on directed and open tutoring. This approach has received overwhelmingly positive responses from students regarding the effectiveness of pedagogical approaches (i.e., Exemplar Vignettes, content tutoring), assessment models (i.e., electronically delivered quizzes, flexible scheduling, use of testing center), and tutoring strategies (i.e., self-paced, exam results review). Survey results have been positive with respect to student perceptions across a variety of perspectives compared to paper-based delivery. In this manuscript, the BLUESHIFT framework and process, as well as results collected from student performance and perceptions of the initial implementation, will be addressed. © 2019 American Society for Engineering Education. All rights reserved.

关键词： Students

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Formal methods and software engineering for DL Security, safety and productivity for DL systems development

arXiv

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arXiv 2019年

作者： Hains, Gaétan J.D.R. Jakobsson, Arvid Khmelevsky, Youry Huawei Parallel and Distributed Algorithms Lab. Huawei Paris Research Center Boulogne Billancourt France LACL Univ. Paris-Est Créteil France LIP6 Database and Machine Learning Department Sorbonne University Paris France Comp.Sc. Okanagan College Canada

Deep learning (DL) techniques are now widespread and being integrated into many important systems. Their classification and recognition abilities ensure their relevance for multiple application domains. As machine-learning that relies on training instead of algorithm programming, they offer a high degree of productivity. But they can be vulnerable to attacks and the verification of their correctness is only just emerging as a scientific and engineering possibility. This paper is a major update of a previously-published survey, attempting to cover all recent publications in this area. It also covers an even more recent trend, namely the design of domain-specific languages for producing and training neural nets. Copyright © 2019, The Authors. All rights reserved.

关键词： Productivity

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QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

arXiv

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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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Glass: Group learning at significant scale via WiFi-enabled learner design teams in an ECE flipped classroom 124

Glass: Group learning at significant scale via WiFi-enabled ...

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124th ASEE Annual Conference and Exposition

作者： Demara, Ronald F. Salehi, Soheil Chen, Baiyun Hartshorne, Richard Department of Electrical and Computer Engineering University of Central Florida OrlandoFL32816-2362 United States Instructional Design and Technology Program University of Central Florida OrlandoFL32816-2362 United States Center for Distributed Learning University of Central Florida OrlandoFL32816-2362 United States

The Group learning At Significant Scale (GLASS) approach is developed to increase the scalability and efficacy of student design teams during group sessions of a Flipped Classroom (FC), as well as conventional modality courses. GLASS utilizes freely-available collaboration tools to facilitate instructional delivery, assessment, and review of teams that leverage campus WiFi connectivity, along with a pedagogical approach using excerpts from actual data sheets and open Internet resources. This immersive collaborative design experience is interwoven on a weekly basis with the technical content provided via video during the preceding week. The instructor manages multiple design teams to conduct a weekly Challenge Problem during inclass time. First, students are randomized by the learning Management System into small groups. Second, a challenge problem is provided, delivered via WiFi-enabled laptops, tablets, or smart phones, forming virtual design teams, regardless of where students are seated. Third, students utilize their WiFi enabled devices to discuss the challenge question via chatroom-style dialog channels alongside a solution whiteboard and/or figure drawing space, while utilizing open resources on the Internet to postulate a solution. Fourth, once the design team concurs that their results are complete, they submit their answers to the learning Management System (LMS) for auto-grading and score-recording in the grade book. Credit is earned by correctly answering each designated question sub-part, which provides partial credit. Throughout the team design activity, the instructor monitors the assignment progress online in real-time, including windows for each design team showing a solution draft as it is constructed, and providing feedback via each group's designated chat channel. LMS statistics are available in real-time for the autograded answer of the first design team having a correct solution, dubbed the Pioneer Group, which receives a bonus after its group l

关键词： Students

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Elevating participation and outcomes with computer-based assessments: An immersive development workshop for engineering faculty

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Computers in Education Journal 2017年第3期8卷

作者： DeMara, Ronald F. Chen, Baiyun Hartshorne, Richard Thripp, Richard University of Central Florida United States Center for Distributed Learning UCF United States Instructional Design and Technology program College of Education and Human Performance UCF United States University of Central Florida United States

Using computer-based assessments for engineering and other STEM courses is challenging because it requires authentic assessment items that support partial credit, solution composability/traceability, and creative design aspects. Fortunately, learning management systems (LMSs), such as Canvas or Moodle, can be adapted through creative means to deliver more complex assessment options, including incremental solution, multiple answer, design-by-selection, and dynamic cloning. Unfortunately, due to the complexity of STEM assessments and development interfaces, it can be overwhelming for faculty to digitize assessments without sufficient exemplars, deep coaching, and peer assistance. This paper summarizes an immersive faculty development approach designed to address such concerns. The six-week Assessment Digitization Innovation summer workshop covered techniques and principles of digital assessment and associated pedagogical approaches, and was completed by ten instructors in 2016 and seven in 2017. Combined with a stipend, a course release, and the resources of the university's Evaluation and Proficiency center (EPC), a proctored testing environment allowing students to schedule their quizzes and exams during a testing window specified by the instructor, participants and their teaching assistants were set up to succeed. In post-surveys, they were "very satisfied" with the workshop and agreed that assessment digitization would save time, refocus faculty workloads from low-impact grading to high-impact structured tutoring, improve remediation, and enable them to effectively serve increasing enrollments. Whereas student laptops with lockdown browsers, unused hours of existing computing labs, and dedicated testing centers allow delivery of computer-based assessments using the techniques herein, the methods described can further help to promulgate reduced grading workloads via digitized formative and summative assessments. © 2018 American Society for Engineering Education. Al

关键词： Grading

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Redesigning computer engineering gateway courses using a novel remediation hierarchy 123

Redesigning computer engineering gateway courses using a nov...

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123rd ASEE Annual Conference and Exposition

作者： De Mara, Ronald F. Khoshavi, Navid Pyle, Steven Edison, John Hartshorne, Richard Chen, Baiyun Georgiopoulos, Michael Department of Electrical and Computer Engineering University of Central Florida OrlandoFL32816-2362 United States Instructional Design and Technology Program University of Central Florida OrlandoFL32816-2362 United States Center for Distributed Learning University of Central Florida OrlandoFL32816-2362 United States College of Engineering and Computer Science University of Central Florida OrlandoFL32816-2362 United States

Focusing on the quality of education, skills, and employability of our graduates in computing-related fields, this work proposes a cost-effective approach to achieve these goals. The Evaluation and Proficiency Infrastructure, Curricula, and Services (EPICS) extends successful methods in other units on campus in an innovative way as synergistic combination of faculty, Graduate Teaching Assistants (GTAs), and educational technology using the Canvas Webcourses learning Management System (LMS) already in use at our college. We integrate computer-based evaluation with a close-knit review and learning cycle based on directed and open tutoring to collectively form the Evaluation and Proficiency center (EPC). Initial results have been encouraging, as students' test scores and survey results indicate a 43% reduction in D or F grades compared to a section of the same course with the same instructor, using conventional delivery, and overwhelmingly positive responses from students regarding the effectiveness of pedagogical strategies (i.e., Exemplar Vignettes, content tutoring), assessment models (i.e., electronically delivered quizzes, flexible scheduling, use of testing center), and tutoring strategies (i.e., self-paced, exam results review). In this manuscript, the EPICS process, as well as results collected from student performance and perceptions of the initial implementation, will be addressed. © American Society for Engineering Education, 2016.

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Author Correction: A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic

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Nature human behaviour 2022年第9期6卷 1318-1319页

作者： Ke Wang Amit Goldenberg Charles A Dorison Jeremy K Miller Andero Uusberg Jennifer S Lerner James J Gross Bamikole Bamikole Agesin Márcia Bernardo Olatz Campos Luis Eudave Karolina Grzech Daphna Hausman Ozery Emily A Jackson Elkin Oswaldo Luis Garcia Shira Meir Drexler Anita Penić Jurković Kafeel Rana John Paul Wilson Maria Antoniadi Kermeka Desai Zoi Gialitaki Elizaveta Kushnir Khaoula Nadif Olalla Niño Bravo Rafia Nauman Marlies Oosterlinck Myrto Pantazi Natalia Pilecka Anna Szabelska I M M van Steenkiste Katarzyna Filip Andreea Ioana Bozdoc Gabriela Mariana Marcu Elena Agadullina Matúš Adamkovič Marta Roczniewska Cecilia Reyna Angelos P Kassianos Minja Westerlund Lina Ahlgren Sara Pöntinen Gabriel Agboola Adetula Pinar Dursun Azuka Ikechukwu Arinze Nwadiogo Chisom Arinze Chisom Esther Ogbonnaya Izuchukwu L G Ndukaihe Ilker Dalgar Handan Akkas Paulo Manuel Macapagal Savannah Lewis Irem Metin-Orta Francesco Foroni Megan Willis Anabela Caetano Santos Aviv Mokady Niv Reggev Merve A Kurfali Martin R Vasilev Nora L Nock Michal Parzuchowski Mauricio F Espinoza Barría Marek Vranka Markéta Braun Kohlová Ivan Ropovik Mikayel Harutyunyan Chunhui Wang Elvin Yao Maja Becker Efisio Manunta Gwenael Kaminski Jordane Boudesseul Dafne Marko Kortnee Evans David M G Lewis Andrej Findor Anais Thibault Landry John Jamir Benzon Aruta Manuel S Ortiz Zahir Vally Ekaterina Pronizius Martin Voracek Claus Lamm Maurice Grinberg Ranran Li Jaroslava Varella Valentova Giovanna Mioni Nicola Cellini Sau-Chin Chen Janis Zickfeld Karis Moon Habiba Azab Neil Levy Alper Karababa Jennifer L Beaudry Leanne Boucher W Matthew Collins Anna Louise Todsen Kevin van Schie Jáchym Vintr Jozef Bavolar Lada Kaliska Valerija Križanić Lara Samojlenko Razieh Pourafshari Sandra J Geiger Julia Beitner Lara Warmelink Robert M Ross Ian D Stephen Thomas J Hostler Soufian Azouaghe Randy McCarthy Anna Szala Caterina Grano Claudio Singh Solorzano Gulnaz Anjum William Jimenez-Leal Maria Bradford Laura Calderón Pérez Julio E Cruz Vásquez Oscar J Galindo-Caballero Juan Cami Harvard Kennedy School Harvard University Cambridge MA USA. Harvard Business School Harvard University Cambridge MA USA. Kellogg School of Management Northwestern University Evanston IL USA. Department of Psychology Willamette University Salem OR USA. millerj@willamette.edu. Institute of Psychology University of Tartu Tartu Estonia. Harvard Kennedy School and Department of Psychology Harvard University Cambridge MA USA. Department of Psychology Stanford University Stanford CA USA. Adekunle Ajasin University Akungba Akoko Nigeria. Faculdade de Psicologia e Ciências da Educação Universidade do Porto Porto Portugal. University of Deusto Barakaldo Spain. University of Navarra Pamplona Spain. University of Valencia Valencia Spain. Stockholm University Stockholm Sweden. California State University Northridge Los Angeles CA USA. Indiana University of Pennsylvania Indiana PA USA. Universidad de Navarra Pamplona Spain. Department of Cognitive Psychology Institute of Cognitive Neuroscience Ruhr University Bochum Bochum Germany. Kindergarten Kustošija Zagreb Croatia. GC University Lahore Lahore Pakistan. Montclair State University Jersey City NJ USA. European University of Cyprus Nicosia Cyprus. Indiana University of Pennsylvania Akron PA USA. Leiden University Culemborg The Netherlands. Stockholm Sweden. Poggio Imperiale Italy. Barakaldo Spain. Islamabad Pakistan. Gent Belgium. Oxford Internet Institute University of Oxford Oxford UK. Warsaw Poland. Queen's University Belfast Belfast Ireland. Universiteit Leiden Leiden The Netherlands. Institute of Psychology Jagiellonian University Krakow Poland. Department of Psychology Lucian Blaga University of Sibiu Sibiu Romania. Carol Davila University of Medicine and Pharmacy Bucharest Romania. National Research University Higher School of Economics Moscow Russia. Institute of Psychology Faculty of Arts University of Presov Presov Slovakia. Institute of Social Sciences CSPS Sl

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Modeling affective reactions for training adaptive interviewing

Modeling affective reactions for training adaptive interview...

引用

16th Conference on Behavior Representation in Modeling and Simulation 2007, BRIMS

作者： Frank, Geoffrey A. Hubal, Robert C. RTI International Center for Distributed Learning 3040 Cornwallis Road Research Triangle Park NC 27709 United States

ISBN: (纸本)9781615671748

This paper describes the use of personal computer simulations for teaching adaptive interviewing skills. The training applications described here require embedding affective responses by synthetic characters into the interviewing process to encourage adaptive learning. The concept is that the student has to modify his or her approach to completing the interview by taking into account the affective responses of the characters. Simulations that provide variations on basic scenarios designed to meet specific learning objectives allow the student to participate in deliberate practice of subtle interaction skills in the context of requirements to achieve specific interview results. The paper describes three applications of our synthetic character technology: training field interviewers prior to conducting house-to-house surveys, training medical students on trust-building skills with pediatric patients, and training police officers to de-escalate encounters with the mentally ill.

关键词： Personnel training

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