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Frontiers in Education (FIE) Conference

作者： Ahmad D. Suleiman Jan DeWaters Daqing Hou Yu Liu David Shepherd Computing and Information Sciences Rochester Institute of Technology Rochester NY USA Institute for STEM Education Clarkson University Potsdam NY USA Software Engineering Department Rochester Institute of Technology Rochester NY USA Electrical & Computer Engineering Clarkson University Potsdam NY USA Department of Computer Science Louisiana State University Baton Rouge LA USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

In this research paper, we conducted a comparative study to measure the effectiveness of the provided technical support in computing project-based learning (PjBL) courses. Students learn much better by solving authentic real-world problems through PjBL. PjBL in computing education has proven to boost student motivation and engagement while enhancing academic performance. Crucial to PjBL in computing is the technical support that the instructors can provide to students, which is required for sustained, successful learning during project tasks. Without adequate support, PjBL will fall short of accomplishing its goals, leading to a rise in student frustration, a loss of motivation and engagement, and compromised learning outcomes. Measuring the impacts and effectiveness of the provided support is imperative for fostering continuous improvement, informed decision-making, and student success. It enables instructors to assess the impacts of their strategies, improve their approaches, and utilize their resources more effectively. To measure the impacts of technical support on students during PjBL, we performed a comparative study on two undergraduate computing courses in Spring 2024, Fundamentals of Software engineering and Database Systems. In both courses, students work on two assigned projects, one with little and inadequate support and the other with adequate support. We administered a post-survey after each project was completed. We analyzed students' selfreflection responses across four sub-scales, support satisfaction, motivation, self-efficacy, and project satisfaction. The results show a statistically significant increase in the supported project in the Fundamentals of Software engineering course and no difference in the Database Systems course. This finding is likely due to other differences between the two projects for the Database Systems course beyond support, such as project scale. Qualitative analysis of students' responses also indicates the need for suppor

关键词： Surveys Statistical analysis Education Decision making Database systems Software measurement Continuous improvement Springs Software engineering

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Generalized representative structures for atomistic systems

arXiv

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arXiv 2024年

作者： Goff, James M. Mullen, Coreen Yang, Shizhong Starovoytov, Oleg N. Wood, Mitchell A. Center for Computing Research Sandia National Laboratories AlbuquerqueNM87185 United States Department of Mathematics Computer Science and Engineering Technology Elizabeth City State University Elizabeth CityNC27909 United States Computer Science Department Southern University Baton RougeLA70807 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States

A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g., the chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, along with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided here utilizing the atomic cluster expansion (ACE) basis. The N-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure (GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. With the GRS method, we highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40-72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted. © 2024, CC BY.

关键词： Crystal lattices

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Unsupervised Replay Strategies for Continual Learning with Limited Data

arXiv

引用

arXiv 2024年

作者： Bazhenov, Anthony Dewasurendra, Pahan Krishnan, Giri P. Delanois, Jean Erik Del Norte High School San DiegoCA United States Department of Medicine University of California La Jolla San DiegoCA United States Department of Computer Science & Engineering University of California La Jolla San DiegoCA United States

Artificial neural networks (ANNs) show limited performance with scarce or imbalanced training data and face challenges with continuous learning, such as forgetting previously learned data after new tasks training. In contrast, the human brain can learn continuously and from just a few examples. This research explores the impact of’sleep’—an unsupervised phase incorporating stochastic activation with local Hebbian learning rules—on ANNs trained incrementally with limited and imbalanced datasets, specifically MNIST and Fashion MNIST. We discovered that introducing a sleep phase significantly enhanced accuracy in models trained with limited data. When a few tasks were trained sequentially, sleep replay not only rescued previously learned information that had been catastrophically forgetting following new task training but often enhanced performance in prior tasks, especially those trained with limited data. This study highlights the multifaceted role of sleep replay in augmenting learning efficiency and facilitating continual learning in ANNs. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

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SSI meets Metaverse for Industry 4.0 and Beyond

TechRxiv

引用

TechRxiv 2022年

作者： Cali, Umit Ferdous, Md Sadek Karaarslan, Enis Gourisetti, Nikhil Gupta Mylrea, Michael Department of Electric Power Engineering Norwegian University of Science and Technology Trondheim Norway Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Computer Engineering Muğla Sıtkı Koçman University Muğla Turkey Department of Security Architecture National Resilience La Jolla CA United States

As the global industrial complex gears toward fulfilling the tenets of Industry 4.0 and beyond, technologies such as distributed ledger technologies, digital twins, and artificial intelligence become pivotal enablers. In the last decade, metaverse as a concept and technology found its place among crucial enablers for technology and digital advancement across several engineering domains. Metaverse has the potential to combine the elements from distributed computing platforms, the digital evolution of physical systems, and advanced learning systems to unearth a fully digitized world of comparative properties of the real world. We should ensure the privacy, integrity, and confidentiality of personal data. These requirements will lead to proper identity management in the metaverse. Given the complex nature of the metaverse, traditional centralized systems may not offer a viable identity management solution. Therefore, this study explores a decentralized identity management system called the Self-sovereign Identity (SSI) as a logical alternative to traditional centralized identity management systems. The proposed holistic framework aims to ignite new ideas and discussions related to the combined deployment of DLT, SSI, and metaverse to inspire new implementation areas within the Industry 4.0 environment. The paper also discusses various opportunities, enablers, technical & privacy aspects, legislation requirements, and other barriers related to SSI implementation. © 2022, CC BY.

关键词： Industry 4.0

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A New Index based on Power Splitting Indices for Predicting Proper Time of Controlled Islanding

arXiv

引用

arXiv 2023年

作者： Davarikia, Hamzeh Znidi, Faycal Barati, Masoud Rathore, Heena McNeese State University Department of Electrical Engineering Lake CharlesLA United States Department of Electrical Engineering Texas A&M Uuniversity TexarkanaTX United States Department of Electrical & Computer Engineering University of Pittsburgh PittsburghPA United States Department of Computer Science Texas State University San MarcosTX United States

In the event of large disturbances, the practice of controlled islanding is used as a last resort to prevent cascading outages. The application of the strategy at the right time is crucial to maintaining system security. A controlled islanding strategy may be deployed efficiently at the right time by predicting the time of uncontrolled system splitting. The purpose of this study is to predict the appropriate islanding time to prevent catastrophic blackout and uncontrolled islanding based on existing relationships between coherent generator groups. A new instability index is derived from the proximity of inter-area oscillations to power splitting indices. Power splitting indices are derived using synchronization coefficients, which recognize the conditions in the system that warrant controlled islanding. The critical values of indices are calculated in offline mode using simulation data from IEEE 39-Buses, and their online performance is evaluated following a controlled islanding strategy. Through the introduction of these indices, system degradation can be effectively evaluated, and blackouts can be predicted early and prevented by controlled islanding at the right time. © 2023, CC BY.

关键词： Forecasting

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Distributed management of patient data-sharing informed consents for clinical research

引用

computers in Biology and Medicine 2024年 180卷 108956页

作者： Pham, Anh Edelson, Maxim Nouri, Armin Kuo, Tsung-Ting UCSD Health Department of Biomedical Informatics University of California San Diego La Jolla CA United States UCSD Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Department of Biomedical Engineering Fu Foundation School of Engineering Columbia University New YorkNY United States Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States Department of Surgery School of Medicine Yale University New HavenCT United States

Background: The consent protocol is now a critical part in the overall orchestration of clinical research. We aimed to demonstrate the feasibility of an Ethereum-based informed consent system, which includes an immutable and automated channel of consent matching, to simultaneously assure patient privacy and increase the efficiency of researchers’ data access. Method: We simulated a multi-site scenario, each assigned 10000 consent records. A consent record contained one patient's data-sharing preference with regards to seven data categories. We developed a blockchain-based infrastructure with a smart contract to record consents on-chain, and to query consenting patients corresponding to specific criteria. We measured our system's recording efficiency against a baseline design and verified accuracy by testing an exhaustive list of possible queries. Results: Our method achieved ∼3–4% lead with an average insertion speed of ∼2 s per record per node on either a 3-, 4- or 5-node network, and 100 % accuracy. It also outperformed other solutions in external validation. Discussion: The speed we achieved is reasonable in a real-world system under the realistic assumption that patients may not change their minds too frequently, with the added benefit of immutability. Furthermore, the per-insertion time did improve slightly as the number of network nodes increased, attesting to the benefit of node parallelism as it suggests no attrition of insertion efficiency due to scale of nodes. Conclusions: Our work confirms the technical feasibility of a blockchain-based consent mechanism, assuring patients with an immutable audit trail, and providing researchers with an efficient way to reach their cohorts. © 2024

关键词： Clinical research

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Artificial Intelligence for science in Quantum, Atomistic, and Continuum Systems

arXiv

引用

arXiv 2023年

作者： Zhang, Xuan Wang, Limei Helwig, Jacob Luo, Youzhi Fu, Cong Xie, Yaochen Liu, Meng Lin, Yuchao Xu, Zhao Yan, Keqiang Adams, Keir Weiler, Maurice Li, Xiner Fu, Tianfan Wang, Yucheng Strasser, Alex Yu, Haiyang Xie, YuQing Fu, Xiang Xu, Shenglong Liu, Yi Du, Yuanqi Saxton, Alexandra Ling, Hongyi lawrence, Hannah Stärk, Hannes Gui, Shurui Edwards, Carl Gao, Nicholas ladera, Adriana Wu, Tailin Hofgard, Elyssa F. Tehrani, Aria Mansouri Wang, Rui Daigavane, Ameya Bohde, Montgomery Kurtin, Jerry Huang, Qian Phung, Tuong Xu, Minkai Joshi, Chaitanya K. Mathis, Simon V. Azizzadenesheli, Kamyar Fang, Ada Aspuru-Guzik, Alán Bekkers, Erik Bronstein, Michael Zitnik, Marinka Anandkumar, Anima Ermon, Stefano Liò, Pietro Yu, Rose Günnemann, Stephan Leskovec, Jure Ji, Heng Sun, Jimeng Barzilay, Regina Jaakkola, Tommi Coley, Connor W. Qian, Xiaoning Qian, Xiaofeng Smidt, Tess Ji, Shuiwang Department of Computer Science & Engineering Texas A&M University College StationTX United States Department of Chemical Engineering Massachusetts Institute of Technology Cambridge United Kingdom AMLab University of Amsterdam Amsterdam Netherlands Department of Computer Science University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical & Computer Engineering Texas A&M University College StationTX United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United Kingdom Department of Materials Science & Engineering Texas A&M University College StationTX United States Department of Physics & Astronomy Texas A&M University College StationTX United States Department of Applied Mathematics & Statistics Stony Brook University Stony BrookNY United States Department of Computer Science Stony Brook University Stony BrookNY United States Department of Computer Science Cornell University IthacaNY United States Department of Computer Science Technical University of Munich München Germany Department of Computer Science Stanford University StanfordCA United States Department of Computer Science & Engineering University of California San Diego La Jolla CA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Nvidia Santa ClaraCA United States Department of Chemistry and Chemical Biology Harvard University Cambridge United Kingdom Department of Chemistry University of Toronto Toronto Canada Department of Computer Science University of Toronto Toronto Canada Department of Computer Science University of Oxford Oxford United Kingdom Department of Biomedical Informatics Harvard University BostonMA United States Department of Computing & Mathematical Sciences California Institute of Technology PasadenaCA United States Computational Science Initiative Brookhaven National Laboratory UptonNY United States

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4science). Being an emerging research paradigm, AI4science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4science;namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4science. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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SSI meets Metaverse for Industry 4.0 and Beyond

SSI meets Metaverse for Industry 4.0 and Beyond

引用

Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Umit Cali Md Sadek Ferdous Enis Karaarslan Sri Nikhil Gupta Gourisetti Michael Mylrea Department of Electric Power Engineering Norwegian University of Science and Technology Trondheim Norway Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Computer Engineering Muğla Sıtkı Koçman University Muğla Türkiye Department of Security Architecture National Resilience La Jolla CA USA

As the global industrial complex gears toward fulfilling the tenets of Industry 4.0 and beyond, technologies such as distributed ledger technologies, digital twins, and artificial intelligence become pivotal enablers. In the last decade, metaverse as a concept and technology found its place among crucial enablers for technology and digital advancement across several engineering domains. Metaverse has the potential to combine the elements from distributed computing platforms, the digital evolution of physical systems, and advanced learning systems to unearth a fully digitized world of comparative properties of the real world. We should ensure the privacy, integrity, and confidentiality of personal data. These requirements will lead to proper identity management in the metaverse. Given the complex nature of the metaverse, traditional centralized systems may not offer a viable identity management solution. Therefore, this study explores a decentralized identity management system called the Self-sovereign Identity (SSI) as a logical alternative to traditional centralized identity management systems. The proposed holistic framework aims to ignite new ideas and discussions related to the combined deployment of DLT (Distributed Ledger Technology), SSI, and metaverse to inspire new implementation areas within the Industry 4.0 environment. The paper also discusses various opportunities, enablers, technical & privacy aspects, legislation requirements, and other barriers related to SSI implementation.

关键词： Privacy Metaverse Distributed ledger Shape Supply chains Transportation Market research

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Providing Technical Support to Sustain Student Motivation and Engagement in Software engineering Project-Based Learning

Providing Technical Support to Sustain Student Motivation an...

引用

Frontiers in Education (FIE) Conference

作者： Ahmad D. Suleiman David Shepherd Jan DeWaters Yu Liu Daqing Hou Computing and Information Sciences Rochester Institute of Technology Rochester NY USA Department of Computer Science Louisiana State University Baton Rouge LA USA Institute for STEM Education Clarkson University Potsdam NY USA Electrical & Computer Engineering Clarkson University Potsdam NY USA Software Engineering Department Rochester Institute of Technology Rochester NY USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

In this research paper, drawing from our own and other computing instructors' experiences, we highlight common technical challenges faced by students in software engineering project-based learning (PjBL) and discuss ways in which instructors can support students in overcoming them so that motivation is summoned and sustained. Through the use of practical hands-on experiences, PjBL has been shown to be an effective educational approach. However, unless projects are intentionally designed and supported in a way that summons and sustains student motivation, PjBL is likely to fail to accomplish its goals. Several factors influence student motivation, including their perception of the project's value and how confident they are in their ability to complete it. In particular, challenges that students perceive as insurmountable during the project can significantly weaken their motivation. On the other hand, supporting students to overcome such hurdles can be troublesome, especially in large classes as well as classes with diversity in student backgrounds. To generalize from our own experience, we designed a questionnaire targeted at PjBL computing instructors that contained closed and open questions on technical challenges faced by students, support instructors provided to overcome such challenges, and lessons learned by instructors on the effectiveness of their support. A total of 47 responses were collected from instructors with diverse backgrounds in terms of courses taught, students' years, and class sizes. We categorized the technical challenges into three main categories, namely (a) challenges in installing and configuring software packaged tools, (b) lack of prerequisite knowledge, and (c) challenges while completing project tasks. In this paper, we present the survey results from the three categories of technical challenges, their frequencies, importance, and effective support strategies instructors use to alleviate them.

关键词： Surveys Hands Time-frequency analysis Software Software engineering

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