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Improving the learning performance by exploiting multimedia in eXtreme apprenticeship

Multimedia Tools and Applications 2024年 1-25页

作者： Persia, Fabio D’Auria, Daniela Ge, Mouzhi Pilato, Giovanni Department of Information Engineering Computer Science and Mathematics University of L’Aquila Via Vetoio L’Aquila67100 Italy Faculty of Computer Science Free University of Bozen-Bolzano Piazza Domenicani 3 Bozen-Bolzano39100 Italy Faculty of European Campus Rottal-Inn Deggendorf Institute of Technology Dieter-Görlitz-Platz 1 Deggendorf94469 Germany Institute for High Performance Computing and Networking National Research Council of Italy Via Ugo La Malfa 153 Palermo90146 Italy

From prior studies on eXtreme Apprenticeship (XA), it can be seen that XA has emerged as an innovative and effective educational approach. The technology in computer science evolves rapidly and XA tackles the gap between University education and industry development. Additionally, distance learning by multimedia is more and more important, sometimes even becoming necessary. As a result, this paper intends to study how to integrate XA and multimedia to improve the learning performance in computer science, with particular reference to the programming courses. Since computer science is mostly driven by the new applications, XA and multimedia are considered to be effective and efficient to reduce the cognitive efforts by practical demonstration. The experimental results, based on the case study of the operating systems course at the Bachelor’s level, show that exploiting multimedia in XA can significantly improve the learning performance in terms of grade distributions, reliability, knowledge transfer and student satisfaction with respect to more traditional approaches based on frontal lectures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Distance education

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A riemannian exponential augmented lagrangian method for computing the projection robust Wasserstein distance 23

A riemannian exponential augmented lagrangian method for com...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Bo Jiang Ya-Feng Liu Ministry of Education Key Laboratory of NSLSCS School of Mathematical Sciences Nanjing Normal University Nanjing China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

Projection robust Wasserstein (PRW) distance is recently proposed to efficiently mitigate the curse of dimensionality in the classical Wasserstein distance. In this paper, by equivalently reformulating the computation of the PRW distance as an optimization problem over the Cartesian product of the Stiefel manifold and the Euclidean space with additional nonlinear inequality constraints, we propose a Riemannian exponential augmented Lagrangian method (REALM) for solving this problem. Compared with the existing Riemannian exponential penalty-based approaches, REALM can potentially avoid too small penalty parameters and exhibit more stable numerical performance. To solve the subproblems in REALM efficiently, we design an inexact Riemannian Barzilai-Borwein method with Sinkhorn iteration (iRBBS), which selects the stepsizes adaptively rather than tuning the step-sizes in efforts as done in the existing methods. We show that iRBBS can return an ε-stationary point of the original PRW distance problem within O(ε-3) iterations, which matches the best known iteration complexity result. Extensive numerical results demonstrate that our proposed methods outperform the state-of-the-art solvers for computing the PRW distance.

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An Adjusted Gray Map Technique for Constructing Large Four-Level Uniform Designs

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Journal of Systems science & Complexity 2023年第1期36卷 433-456页

作者： ELSAWAH A M VISHWAKARMA G K MOHAMED H S FANG Kai-Tai Department of Statistics and Data Science Faculty of Science and TechnologyBeijing Normal University-Hong Kong Baptist University United International CollegeZhuhai 519087China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International CollegeZhuhai 519087China Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 44519Egypt Department of Mathematics&Computing Indian Institute of Technology DhanbadDhanbad 826004India College of Transportation and Civil Engineering Fujian Agriculture and Forestry UniversityFuzhou 350002China The Key Lab of Random Complex Structures and Data Analysis The Chinese Academy of SciencesBeijing 100190China

A uniform experimental design(UED)is an extremely used powerful and efficient methodology for designing experiments with high-dimensional inputs,limited resources and unknown underlying models.A UED enjoys the following two significant advantages:(i)It is a robust design,since it does not require to specify a model before experimenters conduct their experiments;and(ii)it provides uniformly scatter design points in the experimental domain,thus it gives a good representation of this domain with fewer experimental trials(runs).Many real-life experiments involve hundreds or thousands of active factors and thus large UEDs are *** large UEDs using the existing techniques is an NP-hard problem,an extremely time-consuming heuristic search process and a satisfactory result is not *** paper presents a new effective and easy technique,adjusted Gray map technique(AGMT),for constructing(nearly)UEDs with large numbers of four-level factors and runs by converting designs with s two-level factors and n runs to(nearly)UEDs with 2^(t−1)s four-level factors and 2tn runs for any t≥0 using two simple transformation *** justifications for the uniformity of the resulting four-level designs are given,which provide some necessary and/or sufficient conditions for obtaining(nearly)uniform four-level *** results show that the AGMT is much easier and better than the existing widely used techniques and it can be effectively used to simply generate new recommended large(nearly)UEDs with four-level factors.

关键词： Aberration adjusted Gray map technique Gray map technique Hamming distance moment aberration threshold accepting algorithm uniform design

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A new framework of high-order unfitted finite element methods using ALE maps for moving-domain problems

arXiv

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arXiv 2024年

作者： Lu, Wenhao Ma, Chuwen Zheng, Weiying LSEC NCMIS Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Science University of Chinese Academy of Sciences Beijing100049 China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China

As a sequel to our previous work [C. Ma, Q. Zhang and W. Zheng, SIAM J. Numer. Anal., 60 (2022)], [C. Ma and W. Zheng, J. Comput. Phys. 469 (2022)], this paper presents a generic framework of arbitrary Lagrangian-Eulerian unfitted finite element (ALE-UFE) methods for partial differential equations (PDEs) on time-varying domains. The ALE-UFE method has a great potential in developing high-order unfitted finite element methods. The usefulness of the method is demonstrated by a variety of moving-domain problems, including a linear problem with explicit velocity of the boundary (or interface), a PDE-domain coupled problem, and a problem whose domain has a topological change. Numerical experiments show that optimal convergence is achieved by both third- and fourth-order methods on domains with smooth boundaries, but is deteriorated to the second order when the domain has topological changes. Copyright © 2024, The Authors. All rights reserved.

关键词： Finite element method

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Robust Sensor Fusion Algorithms for IoT

Robust Sensor Fusion Algorithms for IoT

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Subramanian, Sakthitharan Lokeshwari, Gandrakoti Jagadeesan, S. Leela, S. Pratheep, V.G. Venkatasudhahar, Murugesan School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai603203 India Geethanjali College of Engineering and Technology Department of Cybersecurity Telangana Hyderabad501301 India Department of Computer Science and Engineering Tamil Nadu Erode638052 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Kongu Engineering College Department of Mechatronics Engineering Tamil Nadu Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mechanical Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331543617

Sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques. © 2024 IEEE.

关键词： Kalman filters

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Design and analysis of key scheduling algorithm for symmetric cipher security

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3991-3997页

作者： Tiwari, Vikas Kameswari, U. Surya Singh, Ajeet Saxena, Ashutosh Acharya Nagarjuna University Andhra Pradesh Guntur 522510 India C.R. Rao Advanced Institute of Mathematics Statistics and Computer Science University of Hyderabad Campus Telangana Hyderabad 500046 India Dept of Computer Science and Engineering Acharya Nagarjuna University Andhra Pradesh Guntur 522510 India School of Computing Science and Engineering Vellore Institute of Technology VIT Bhopal University Bhopal-Indore Highway Madhya Pradesh Bhopal 466114 India CMR Technical Campus Telangana Hyderabad 501401 India

The success and safety of block cipher systems heavily depend on how efficient and secure their Key Schedule Algorithms (KSAs) are, especially when fighting against cryptanalytic attacks. This paper proposes a novel KSA architecture designed to strike an optimal balance between computational efficiency and cryptographic security. Through the utilization of advanced algorithmic constructs, the proposed KSA minimizes processing time and resource consumption, thus catering to the needs of both high-performance and resource-constrained environments. We conduct a comprehensive statistical analysis to ascertain the computational innovation of our design, employing entropy measures, autocorrelation tests, and non-linear complexity assessments. The empirical evaluation demonstrates the resilience of our KSA against a variety of cryptanalytic benchmarks, revealing significant improvements in key unpredictability and distribution. Additionally, extensive benchmarking against established cryptographic standards confirms the superior performance and security enhancements offered by our KSA. © Bharati Vidyapeeth's institute of Computer Applications and Management 2024.

关键词： Block cipher security Cryptographic design Encryption analysis Key schedule algorithm Secure communication

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Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling

arXiv

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arXiv 2025年

作者： Li, Chaojian Ye, Zhifan Pasini, Massimiliano Lupo Choi, Jong Youl Wan, Cheng Lin, Yingyan Balaprakash, Prasanna Computational Sciences and Engineering Division Oak Ridge National Laboratory United States Computer Science and Mathematics Division Oak Ridge National Laboratory United States Computing and Computational Sciences Directorate Oak Ridge National Laboratory United States Georgia Institute of Technology United States

Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property can lead to significant advancements in scientific discovery. Graph Neural Networks (GNNs) represent the state-of-the-art approach for modeling atomistic material data thanks to their capacity to capture complex relational structures. While machine learning performance has historically improved with larger models and datasets, GNNs for atomistic materials modeling remain relatively small compared to large language models (LLMs), which leverage billions of parameters and terabyte-scale datasets to achieve remarkable performance in their respective domains. To address this gap, we explore the scaling limits of GNNs for atomistic materials modeling by developing a foundational model with billions of parameters, trained on extensive datasets in terabyte-scale. Our approach incorporates techniques from LLM libraries to efficiently manage large-scale data and models, enabling both effective training and deployment of these large-scale GNN models. This work addresses three fundamental questions in scaling GNNs: the potential for scaling GNN model architectures, the effect of dataset size on model accuracy, and the applicability of LLM-inspired techniques to GNN architectures. Specifically, the outcomes of this study include (1) insights into the scaling laws for GNNs, highlighting the relationship between model size, dataset volume, and accuracy, (2) a foundational GNN model optimized for atomistic materials modeling, and (3) a GNN codebase enhanced with advanced LLM-based training techniques. Our findings lay the groundwork for large-scale GNNs with billions of parameters and terabyte-scale datasets, establishing a scalable pathway for future advancements in atomistic materials modeling. Copyright © 2025, The Authors. All rights reserved.

关键词： Materials properties

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Automatic Title Generation with Attention-Based LSTM 3rd

Automatic Title Generation with Attention-Based LSTM

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3rd International Conference on Information Technology, InCITe 2023

作者： Dhilsath Fathima, M. Seeni Syed Raviyathu Ammal, M. Singh, Prashant Kumar Shome, Sachi Kharsyienlieh, Manbha Hariharan, R. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Information Technology Mohamed Sathak Engineering College Tamil Nadu Kilakarai India Department of Information Technology Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819959969

The objective of this article is to develop a generalised architecture that can generate headings for any type of document, including articles, book paragraphs, newspaper articles, and legal documents. The title of a document provides a concise overview of its content, allowing readers to quickly understand the main idea without focusing on the specifics. The more attractive the title is, the more the reader is bound to read it. Title generation will be helpful to the editors of newspapers or technical writers to easily find the central theme of the whole article. The Sequence-to-Sequence (Seq-to-Seq) model is implemented in this proposed work for generating the title for the project, article, and research articles. The Seq-to-Seq model utilizes an encoder-decoder recurrent neural network that has an attention layer and Long Short-Term Memory (LSTM). The main strength of the developed framework is the adoption of an attention layers to the encoder-decoder architecture, which assigns more weightage to background words. This proposed model achieves 90% prediction accuracy by including an attention layer into the LSTM. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Ultrastrong Coupling between Polar Distortion and Optical Properties in Ferroelectric MoBr2O2 (vol 146, pg 15411, 2024)

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JOURNAL OF THE AMERICAN CHEMICAL SOCIETY 2024年第32期146卷 22860-22860页

作者： Li, Zhaojun Varrassi, Lorenzo Yang, Yali Franchini, Cesare Bellaiche, Laurent He, Jiangang School of Mathematics and Physics University of Science and Technology Beijing Beijing 100083 China Department of Physics and Astronomy “Augusto Righi” Alma Mater Studiorum Università di Bologna Bologna 40127 Italy Faculty of Physics and Center for Computational Materials Science University of Vienna Kolingasse 14-16 Vienna 1090 Austria Smart Ferroic Materials Center Physics Department and Institute for Nanoscience and Engineering University of Arkansas Fayetteville Arkansas 72701 United States Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing Beijing 100083 China

Tuning the properties of materials by using external stimuli is crucial for developing versatile smart materials. Strong coupling among the order parameters within a single-phase material constitutes a potent foundation for achieving precise property control. However, cross-coupling is fairly weak in most single materials. Leveraging first-principles calculations, we demonstrate a layered mixed anion compound MoBr2O2 that exhibits electric-field switchable spontaneous polarization and ultrastrong coupling between polar distortion and electronic structures as well as optical properties. It offers feasible avenues of achieving tunable Rashba spin-splitting, electrochromism, thermochromism, photochromism, and nonlinear optics by applying an external electric field to a single domain sample and heating, as well as intense light illumination. Additionally, it exhibits an exceptionally large photostrictive effect. These findings not only showcase the feasibility of achieving multiple order parameter coupling within a single material but also pave the way for comprehensive applications based on property control, such as energy harvesting, information processing, and ultrafast control.

关键词： Chemical structure Light Materials Phase transitions Polarization

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Cloud-Powered Neural Networks for Sustainable Fishing Practices and Stock Management System 4

Cloud-Powered Neural Networks for Sustainable Fishing Practi...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Suresh, M. Xavier Prathik, A. Arun, V. Venugopal, R. Ganeshbabu, T.R. Muthulekshmi, M. School of Advanced Sciences and Languages Vit Bhopal University Department of Physics Madhya Pradesh Bhopal India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Department of Mathematics Tamil Nadu Salem India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Sustainable fishing methods and efficient stock management are crucial considering growing environmental concerns;fishing practices and effective stock management are imperative. This research presents a novel approach that uses neural networks (NN) hosted in the cloud to transform sustainable fishing and stock management. This method trains NN to make predictions by combining several data sources, such as satellite images, weather patterns, and catch records. These models can reliably predict fish stocks' dynamics, giving stakeholders the information needed to make informed decisions. Crucial for real-time processing and storage of massive information is the scalability and flexibility offered by cloud infrastructure. This framework provides the appropriate usage of resources while simultaneously optimizing fishing tactics, which helps protect marine ecosystems. It demonstrates that method promotes sustainable fishing via thorough case studies and simulations. It provides a flexible and scalable solution to the problems confronting marine biodiversity and the fishing industry by combining advanced technology with ecological principles. To ensure a more egalitarian and sustainable future for fisheries management on a global scale, method is a giant leap forward in balancing commercial interests with environmental protection. Integrating data, ensuring sensors are accurate, managing huge datasets, managing regulatory compliance, and encouraging stakeholders to work together for sustainable practices are all challenges. © 2024 IEEE.

关键词： Fisheries

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