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Evaluation of Instructional Module Development System

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Evaluation of Instructional Module Development System

Frontiers in Education (FIE) Conference

作者： Vaishnavi Raj Preston Goulet Srividya Bansal School of Computing Informatics and Decision Systems Engineering Arizona State University Mesa AZ USA

ISBN: (纸本)9781538611753;9781538611746

This Research full paper addresses the challenge of creating an effective course design that has tight alignment between various course components and incorporates appropriate pedagogy and assessment techniques. Many systems have been proposed solely for the purpose of guiding instructors through the process of course design so as to achieve maximum student learning. One such system is the Instructional Module Development System (IMODS). IMODS is a web-based software system that has been developed to assist instructors in curriculum design based on the principles of Outcome-based Education (OBE) and Bloom's Taxonomy. It is not just enough to come up with a model that theoretically facilitates effective result-oriented course design. There should be facts, experiments and proof that any model succeeds in achieving what it aims to achieve. This paper focuses on testing and evaluation of the IMODS platform and deliver feedback to its development team. We present our methodology and instruments used for testing and evaluation of the tool. Data collection and analysis is described and the results of evaluation of IMODS are presented.

关键词： Tools Education Taxonomy Instruments Testing Usability Task analysis

Estimation of Types of States in Partial Observable Network systems

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Estimation of Types of States in Partial Observable Network ...

IEEE International Conference on Services Economics (SE)

作者： Sayantan Guha School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe Arizona USA

Estimating of types of states in network systems is essential for protecting network infrastructures from cyberattacks, managing network traffic, and detecting changes in network systems. It is very difficult to estimate the types of states in network systems due to their high complexity. The accuracy of the estimating the states in network systems depends heavily on the completeness of the collected sensor information. But the state of a network system at a given point in time may be never fully known due to noisy sensors; making more difficult to estimate the entire true state of a network system because certain features of the input data may be missing. In order to estimate the states in a network system in partially observable environments, an approach to estimating the types of states in partially observable cyber systems is presented. This approach involves the use of a convolutional neural network (CNN), and unsupervised learning with elbow method and k-means clustering algorithm.

关键词： Feature extraction Clustering algorithms Testing Elbow Security Payloads Hidden Markov models

A probabilistic extension of action language BC+

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arXiv 2018年

作者： Lee, Joohyung Wang, Yi School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States

We present a probabilistic extension of action language BC+. Just like BC+ is defined as a high-level notation of answer set programs for describing transition systems, the proposed language, which we call pBC+, is defined as a high-level notation of LPMLN programs-a probabilistic extension of answer set programs. We show how probabilistic reasoning about transition systems, such as prediction, postdiction, and planning problems, as well as probabilistic diagnosis for dynamic domains, can be modeled in pBC+ and computed using an implementation of LPMLN. Copyright © 2018, The Authors. All rights reserved.

关键词： High level languages

A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

How shall i drive? Interaction modeling and motion planning towards empathetic and socially-graceful driving

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arXiv 2019年

作者： Ren, Yi Elliott, Steven Wang, Yiwei Yang, Yezhou Zhang, Wenlong Department of Mechanical and Aerospace Engineering Arizona State University TempeAZ85287 United States School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Polytechnic School Ira A. Fulton Schools of Engineering Arizona State University MesaAZ85212 United States

While intelligence of autonomous vehicles (AVs) has significantly advanced in recent years, accidents involving AVs suggest that these autonomous systems lack gracefulness in driving when interacting with human drivers. In the setting of a two-player game, we propose model predictive control based on social gracefulness, which is measured by the discrepancy between the actions taken by the AV and those that could have been taken in favor of the human driver. We define social awareness as the ability of an agent to infer such favorable actions based on knowledge about the other agent's intent, and further show that empathy, i.e., the ability to understand others' intent by simultaneously inferring others' understanding of the agent's self intent, is critical to successful intent inference. Lastly, through an intersection case, we show that the proposed gracefulness objective allows an AV to learn more sophisticated behavior, such as passive-aggressive motions that gently force the other agent to yield. Copyright © 2019, The Authors. All rights reserved.

关键词： Motion planning

Neural embeddings of scholarly periodicals reveal complex disciplinary organizations

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arXiv 2020年

作者： Peng, Hao Ke, Qing Budak, Ceren Romero, Daniel M. Ahn, Yong-Yeol School of Information University of Michigan United States Center for Complex Network Research Northeastern University United States Center for the Study of Complex Systems University of Michigan United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University United States Indiana University Network Science Institute United States Connection Science Massachusetts Institute of Technology United States

Understanding the structure of knowledge domains is one of the foundational challenges in science of science. Here, we propose a neural embedding technique that leverages the information contained in the citation network to obtain continuous vector representations of scientific periodicals. We demonstrate that our periodical embeddings encode nuanced relationships between periodicals as well as the complex disciplinary and interdisciplinary structure of science, allowing us to make cross-disciplinary analogies between periodicals. Furthermore, we show that the embeddings capture meaningful "axes" that encompass knowledge domains, such as an axis from "soft" to "hard" sciences or from "social" to "biological" sciences, which allow us to quantitatively ground periodicals on a given dimension. By offering novel quantification in science of science, our framework may in turn facilitate the study of how knowledge is created and organized. © 2020, CC BY.

关键词： Embeddings

A Survey on the E-learning platforms used during COVID-19

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A Survey on the E-learning platforms used during COVID-19

International Conference and Workshop on computing and Communication (IEMCON)

作者： Xianping Wang Weiru Chen Hao Qiu Awad Eldurssi Fei Xie Jiayue Shen Purdue University Northwest Hammond IN USA Old Dominion University Norfolk VA USA Fort Valley State University Fort Valley GA USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AR USA College of Engineering SUNY Polytechnic Institute Utica NY USA

ISBN: (数字)9781728184166

ISBN: (纸本)9781728184173

E-learning is globalized in a short period of time due to the physical distancing requirement for curbing the spread of COVID-19. There are two major challenges imposed by the migration from face-to-face learning to E-learning: live lecturing and lab sessions. In this survey, we studied the E-learning platforms used in the COVID-19 pandemic: learning management systems or course management systems, tools for live-lecturing and tools for lab sessions. We found that this migration is quite smooth with contemporary technologies and the Internet. Prescriptive suggestions are also provided for this migration. For the future trend of education, it is expected that E-learning will become a preferable education and learning style in the future, especially for self-study and life-long learning.

关键词： COVID-19 Learning management systems Electronic learning Tools Mobile communication Market research Internet

Privacy, Data Rights and Cybersecurity: Technology for Good in the Achievement of Sustainable Development Goals

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Privacy, Data Rights and Cybersecurity: Technology for Good ...

International Symposium on Technology and Society (ISTAS)

作者： Katina Michael Shannon Kobran Roba Abbas Salah Hamdoun School for the Future of Innovation in Society & Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States SDG Academy Sustainable Development Solutions Network New York United States School of Management Operations and Marketing Faculty of Business University of Wollongong Wollongong Australia

When technology is used for good in addressing sustainable development goals, fundamental human rights are adhered to. In the context of technology, we can discuss privacy, data rights and cybersecurity as three areas that are integral in maintaining the freedom and dignity of the individual. In a rush to bring developing nations on par with developed nations, the rapid deployment of technology is often seen as the answer to the achievement of all 17 sustainable development goals. We have seen, for example, biometric systems deployed in India through the Aadhaar biometric ID system versus the adoption of basic mobile technologies in Africa, providing e-payment capabilities. How can we know whether the deployment of new technology will help or inhibit the liberation of peoples, for example, to conduct mobile commerce? This paper emphasizes the need for three ethical elements- privacy, data rights and cybersecurity- in the deployment of new technologies and provides examples throughout history that demonstrate positive or negative applications of technology.

关键词： Privacy Sustainable development Data privacy Government Computer security History

OER: Six Perspectives on Global Misconceptions and Challenges

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OER: Six Perspectives on Global Misconceptions and Challenge...

2019 IEEE International Conference on engineering, Technology and Education, TALE 2019

作者： Towey, Dave Reisman, Sorel Chan, Henry Demartini, Claudio Tovar, Edmundo Margaria, Tiziana University of Nottingham Ningbo China School of Computer Science Ningbo China California State University Fullerton Department of Information Systems and Decision Sciences Fullerton United States Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong Politecnico di Torino Control Computer Engineering Department Turin Italy Universidad Politécnica de Madrid School of Computer Engineers Madrid Spain University of Limerick Department of Computer Science and Information Systems Limerick Ireland

ISBN: (纸本)9781728126654

Open Educational Resources (OERs) are freely accessible materials for teaching, learning, and research that have been made available such that they can be freely used, modified, and shared. Prompted by the potential positive impact that OERs can have, a growing community of OER enthusiasts has been advocating for their adoption in higher education, and elsewhere. The growing demand for computer science education and training (especially in areas related to artificial intelligence, machine learning, and big data) has led to speculation as to how OERs may be best leveraged to support such teaching and learning. The recent occasion of an international OER workshop at the 2019 IEEE Conference on Computers, Software and Applications (COMPSAC) allowed for six OER enthusiasts, all with extensive OER experience, to reflect on the status of OER adoption and understanding. These reflections, addressing misconceptions and challenges, form the basis for this paper. © 2019 IEEE.

关键词： Application programs