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arXiv 2022年

作者： Mariani, Stefano Picone, Marco Ricci, Alessandro Department of Sciences and Methods of Engineering University of Modena and Reggio Emilia Italy Department of Computer Science and Engineering University of Bologna Italy

Digital Twins (DTs) are rapidly emerging as a fundamental brick of engineering cyber-physical systems, but their notion is still mostly bound to specific business domains (e.g. manufacturing), goals (e.g. product design), or application domains (e.g. the Internet of Things). As such, their value as general purpose engineering abstractions is yet to be fully revealed. In this paper, we relate DTs with agents and multiagent systems, as the latter are arguably the most rich abstractions available for the engineering of complex socio-technical and cyber-physical systems, and the former could both fill in some gaps in agent-oriented engineering and benefit from an agent-oriented interpretation—in a cross-fertilisation journey. © 2022, CC BY.

关键词： Multi agent systems

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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Force-Guided Bridge Matching for Full-Atom Time-Coarsened Dynamics of Peptides

arXiv

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arXiv 2024年

作者： Yu, Ziyang Huang, Wenbing Liu, Yang Department of Computer Science and Technology Tsinghua University China Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Molecular Dynamics (MD) is crucial in various fields such as materials science, chemistry, and pharmacology to name a few. Conventional MD software struggles with the balance between time cost and prediction accuracy, which restricts its wider application. Recently, data-driven approaches based on deep generative models have been devised for time-coarsened dynamics, which aim at learning dynamics of diverse molecular systems over a long timestep, enjoying both universality and efficiency. Nevertheless, most current methods are designed solely to learn from the data distribution regardless of the underlying Boltzmann distribution, and the physics priors such as energies and forces are constantly overlooked. In this work, we propose a conditional generative model called Force-guided Bridge Matching (FBM), which learns full-atom time-coarsened dynamics and targets the Boltzmann-constrained distribution. With the guidance of our delicately-designed intermediate force field, FBM leverages favourable physics priors into the generation process, giving rise to enhanced simulations. Experiments on two datasets consisting of peptides verify our superiority in terms of comprehensive metrics and demonstrate transferability to unseen systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecular dynamics

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An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics

An Automatic CNN-based Face Mask Detection Algorithm Tested ...

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2022 International Conference of Yearly Reports on Informatics, Mathematics, and Engineering, ICYRIME 2022

作者： De Magistris, Giorgio Iacobelli, Emanuele Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

关键词： COVID-19

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Engineering Students' Attitudes and Perceptions towards Project-Based Learning: A Study from Kerala, Southern India 13

Engineering Students' Attitudes and Perceptions towards Proj...

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13th IEEE International Conference on teaching, Assessment and Learning for Engineering, TALE 2024

作者： Akshay, R.S. Sunny, Alviya Saranya, V.S. John, Aby Subheesh, N.P. Anzar, S.M. Government Engineering College Department of Electronics and Communication Engineering Trivandrum India Model Engineering College Department of Computer Science and Engineering India Accenture Solutions Pvt Ltd Coimbatore India Rudn University Faculty of Philology Moscow Russia Srm University - Ap Teaching Learning Centre Andhra Pradesh 522502 India Tkm College of Engineering Department of Electronics and Communication Engineering Kollam India

ISBN: (纸本)9798350376234

The shift from teacher-centric to student-centric education has been a subject of debate, with traditional instruction often criticized for fostering rote learning and passive engagement. Due to its emphasis on student autonomy and real-world application of knowledge, the growing popularity of Project-Based Learning as a novel alternative to traditional classroom-based teaching pedagogy is attracting educational institutions all around the world. In engineering education, multifaceted skill development is paramount. PjBL offers a dynamic approach to cultivate essential competencies and it significantly improves engineering education. The National Education Policy 2020 (NEP 2020) of India lays a strong emphasis on Project-Based Learning as the cornerstone of educational reform. But, only smaller number of research studies are conducted in the country to explore the attitudes and perceptions of students towards Project-Based Learning in the engineering sector. It lays bare the imminent need for educationalists to focus on this research gap. This paper explores the attitudes and perceptions of engineering students towards Project-Based Learning from Kerala, a state in Southern India. A novel questionnaire entitled Project-Based Learning in Engineering Education in Kerala (PLEEK) is developed by the IEEE Education Society Kerala Chapter to conduct the survey. A total of 136 students from all over Kerala attended the survey. It is observed that the students have a positive perception of Project-Based Learning and its impact on enhancing their education. On the other hand, an abysmally low count of 11% of students believed that their current campus ecosystem supported this teaching pedagogy. They highlighted the need for a drastic transformation in the present academic infrastructure to support Project-Based Learning. The findings of the study underscore the potential of Project-Based Learning in enhancing learning outcomes, fostering critical skills, and preparing studen

关键词： Engineering education

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A Study About the Impact of Rewards on Student's Engagement with the Flipped Classroom Methodology

A Study About the Impact of Rewards on Student's Engagement ...

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IEEE Education Engineering (EDUCON)

作者： Jaime Urquiza-Fuentes Department of Computer Science & Statistics ACTIVATE Teaching Innovation Group & Laboratory of Information Technologies in Education - LITE Rey Juan Carlos University Móstoles Madrid Spain

This work describes an incremental design of a course that implements the flipped classroom methodology. The last phase of this implementation was to include rewards based on self-evaluation tests that would impact the students' grades. This should foster students' engagement with the learning methodology. In this work, a deep analysis of the impact of the reward is performed. The analysis of the use of the out-class materials shows that the students' pace does not correspond to the flipped classroom methodology. The students' behaviour is more focused on achieving rewards rather than preparing in-class activities with out-class materials. It is hypothesized that, the main reasons for these behaviours could be that, on the one hand, since the reward is considered by students as another item of the course grade, they work to get the reward. On the other hand, students do not want to change their study methodology because getting involved in the flipped methodology requires from them a significant effort.

关键词： Resistance Electronic learning Education Online services Engineering education Faces Videos

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Multimodal Learning for Predicting Mortality in Patients with Pulmonary Arterial Hypertension

Multimodal Learning for Predicting Mortality in Patients wit...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Suvon, Mohammod N. I. Tripathi, Prasun C. Alabed, Samer Swift, Andrew J. Lu, Haiping The University of Sheffield Department of Computer Science Sheffield United Kingdom Department of Infection Immunity and Cardiovascular Disease INSIGNEO Institute for in Silico Medicine The University of Sheffield Sheffield United Kingdom Department of Clinical Radiology Sheffield Teaching Hospitals Sheffield United Kingdom

ISBN: (纸本)9781665468190

Pulmonary Arterial Hypertension (PAH) is a lifethreatening disorder. The prediction of mortality in PAH patients can play a crucial role in the clinical management of this disease. The prediction of mortality from one modality is a difficult task that may only provide limited performance. Therefore, we propose a multimodal learning approach in this work to predict one-year mortality in PAH patients. We have utilised three modalities, which include extracted numerical imaging features, echo report categorical features, and echo report text features from Electronic Health Records (EHRs) of patients. We have proposed a feature integration module to combine features from multiple modalities. The text features have been extracted from the echo reports using the Bidirectional Encoder Representations from Transformers (BERT). An attention mechanism and a weighted summation method are also adopted during the process of feature integration. We have performed different experiments to evaluate the performance of the proposed framework for mortality prediction. The experimental results indicate that we can achieve the best AUC score of 0.89 for predicting one-year mortality by combining all three modalities. The source code of this paper is available at https://***/Mdnaimulislam/MultimodalTab. © 2022 IEEE.

关键词： Forecasting

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A KALMAN FILTER MODEL FOR SYNCHRONIZATION IN MUSICAL ENSEMBLES

arXiv

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arXiv 2024年

作者： Carvalho, Hugo T. Li, Min S. di Luca, Massimiliano Wing, Alan M. Department of Statistical Methods Federal University of Rio de Janeiro Brazil Bristol Interaction Group School of Computer Science University of Bristol United Kingdom Virtual Reality Lab School of Psychology University of Birmingham United Kingdom

The synchronization of motor responses to rhythmic auditory cues is a fundamental biological phenomenon observed across various species. While the importance of temporal alignment varies across different contexts, achieving precise temporal synchronization is a prominent goal in musical performances. Musicians often incorporate expressive timing variations, which require precise control over timing and synchronization, particularly in ensemble performance. This is crucial because both deliberate expressive nuances and accidental timing deviations can affect the overall timing of a performance. This discussion prompts the question of how musicians adjust their temporal dynamics to achieve synchronization within an ensemble. This paper introduces a novel feedback correction model based on the Kalman Filter, aimed at improving the understanding of interpersonal timing in ensemble music performances. The proposed model performs similarly to other linear correction models in the literature, with the advantage of low computational cost and good performance even in scenarios where the underlying tempo varies. © 2024, CC BY.

关键词： Kalman filters

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Hybrid Machine Learning Classification for Tipple Negative Breast Cancer

Hybrid Machine Learning Classification for Tipple Negative B...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Hend A. Bakr Amira M. Abozaid Ahmed B. Zaky Biomedical Engineering Department Cairo University Giza Egypt Computer Science and Information Technology Programs (CSIT) Egypt Japan University of Science and Technology Molecular Biology division Teaching assistant at Faculty of Science Alexandria University Faculty of Engineering at Shubra Benha University Egypt

This research introduces an innovative approach to dissecting Triple-Negative Breast Cancer (TNBC) by integrating count-based RNA analysis and machine learning techniques. Through the strategic amalgamation of K-means clustering, Convolutional Neural Network (CNN), and Support Vector Machine (SVM), this study transcends conventional classification methods, revealing detailed molecular insights within the complex TNBC landscape. The methodology initiates with count-based RNA analysis for dimensionality reduction, refining molecular classifications through K-means clustering. The incorporation of CNN allows for nuanced feature extraction, capturing intricate genomic relationships, while SVM enhances subtype predictions with precise classification. The addition of Gene Ontology (GO) and pathway analyses enriches our understanding by unraveling the functional implications of identified gene clusters. This synergistic methodology not only advances our comprehension of TNBC subtypes but also establishes a groundwork for targeted therapeutic strategies. The fusion of count-based RNA analysis and machine learning enables a comprehensive and nuanced exploration of TNBC complexity, offering valuable insights for personalized oncology approaches in the era of precision medicine

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Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

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computer Systems science & Engineering 2023年第9期46卷 3321-3338页

作者： Abdelwahed Motwakel Badriyya B.Al-onazi Jaber S.Alzahrani Sana Alazwari Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Amgad Atta Abdelmageed Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Arabic is the world’s first language,categorized by its rich and complicated grammatical ***,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and *** Arabic language consists of distinct variations utilized in a community and particular *** media sites are a medium for expressing opinions and social phenomena like racism,hatred,offensive language,and all kinds of verbal *** conduct does not impact particular nations,communities,or groups only,extending beyond such areas into people’s everyday *** study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic *** presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social *** the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and ***,data pre-processing is performed to transform the Arabic social media text into a useful *** addition,the word2vec word embedding process is utilized to produce word *** attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification ***,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier *** illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were *** extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.

关键词： Hate speech offensive speech Arabic corpora natural language processing social networks

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