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Information Technology (ACIT)

作者： Omar Khair Alla Alidmat Raed Alazaidah Mohammad Arabiat Hayel Khafajeh Mohammad Aljaidi Umi Kalsom Yusof Ali Elrashidi Computer Science Department Zarqa University Zarqa Jordan Data Science and Artifical Inteligence Department Zarqa University Zarqa Jordan Faculty of Information Technology Zarqa University Zarqa Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

This research introduces an innovative methodology to address the significant challenge of evacuating large crowds from buildings with multiple exits, particularly in scenarios with asymmetrical exit configurations and high population densities. The proposed model incorporates dynamic counting areas that adjust according to evacuee density, alongside a probabilistic exit selection mechanism influenced by enhanced dynamic parameters—congestion, distance, and empty. It also accounts for heterogeneous agent behaviors to more accurately represent evacuee dynamics in complex, asymmetrical environments with dense crowds. Key findings indicate that the model effectively alleviates bottlenecks at congested exits, optimizes exit usage, and enhances overall evacuation safety. Comparative analyses demonstrate that the proposed model significantly improves evacuation efficiency and reduces travel distances compared to previous models. Simulation results indicate that agent density directly affects evacuation time, with lower congestion levels facilitating faster egress. These advancements lead to more precise evacuation time predictions and crowd flow assessments, offering valuable insights for refining emergency egress strategies in complex settings. Future research will focus on refining the model with real-world data to expand its applicability in diverse settings, such as stadiums and public transportation hubs, thereby enhancing emergency preparedness and response.

关键词： Analytical models Simulation Refining Layout Probabilistic logic data models Safety Information technology Public transportation Population density

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Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems

arXiv

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arXiv 2025年

作者： Scherbela, Michael Gao, Nicholas Grohs, Philipp Günnemann, Stephan Faculty of Mathematics University of Vienna Oskar-Morgenstern-Platz 1 ViennaA-1090 Austria Department of Computer Science Munich Data Science Institute Technical University of Munich Germany Johann Radon Institute for Computational and Applied Mathematics Austrian Academy of Sciences Altenbergerstrasse 69 Linz4040 Austria

We present finite-range embeddings (FiRE), a novel wave function ansatz for accurate large-scale ab-initio electronic structure calculations. Compared to contemporary neural-network wave functions, FiRE reduces the asymptotic complexity of neural-network variational Monte Carlo (NN-VMC) by ∼ nel, the number of electrons. By restricting electron-electron interactions within the neural network, FiRE accelerates all key operations – sampling, pseudopotentials, and Laplacian computations – resulting in a real-world 10× acceleration in now-feasible 180-electron calculations. We validate our method’s accuracy on various challenging systems, including biochemical compounds, conjugated hydrocarbons, and organometallic compounds. On these systems, FiRE’s energies are consistently within chemical accuracy of the most reliable data, including experiments, even in cases where high-accuracy methods such as CCSD(T), AFQMC, or contemporary NN-VMC fall short. With these improvements in both runtime and accuracy, FiRE represents a new ‘gold-standard’ method for fast and accurate large-scale ab-initio calculations, potentially enabling new computational studies in fields like quantum chemistry, solid-state physics, and material design. Copyright © 2025, The Authors. All rights reserved.

关键词： Organometallics

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Construction Equipment Performance with Cloud data Analysis for Predictive Maintenance 6

Construction Equipment Performance with Cloud Data Analysis ...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Seeni, Senthil Kumar Maheswari, P. Konidhala, Jothibabu Suguna, M. Raja Ishwarya, M.V. Srinivasan, S. Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science and Technology Faculty of Management Studies Kattankulathur Chennai India University of Technology and Applied Sciences- Al Musannah Department of Electronics and Communication Engineering South Al Batina Musanna Oman Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai 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)9798350381689

The research uses Internet of Things technology for predictive maintenance in the construction sector. The system addresses incorporating Internet of Things sensors into construction machinery to enable real-time monitoring of operating parameters. The study suggests a proactive maintenance strategy that may anticipate equipment breakdowns by examining the data obtained and using sophisticated prediction models. With this strategy, construction teams can arrange maintenance activities systematically, which cuts down on downtime and helps avoid expensive interruptions. The abstract emphasizes the advantages of this practice, which include reducing unexpected downtime as much as possible, extending the life of equipment, and making the most of available resources. The study proves the usefulness of the IoT-based predictive maintenance approach via empirical investigations and the analysis of case studies. This validates the strategy's efficacy, contributing to enhanced equipment performance and increased work efficiency in the construction industry. © 2024 IEEE.

关键词： Predictive maintenance

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Early Stage Design of a mHealth Intervention for Managing Gestational Diabetes Mellitus in Bangladeshi Women 25th

Early Stage Design of a mHealth Intervention for Managing...

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25th International Conference on Human-computer Interaction, HCII 2023

作者： Islam, Ashraful Ahmed, Eshtiak Islam, Md Rakibul EMPATHY Lab Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Computer Science Department University of Wisconsin - Eau Claire Eau ClaireWI United States

ISBN: (纸本)9783031492112

Gestational Diabetes Mellitus (GDM) is a rising concern worldwide, particularly in low-resource countries such as Bangladesh, where access to healthcare facilities is limited and awareness of GDM management is inadequate. This work proposes an early-stage design of user interfaces (UIs) for a mobile health (mHealth) intervention using the Bengali language that allows users to input daily measures of blood glucose, weight, and other GDM-related health data. The intervention is designed to provide users with personalized feedback based on their input and the necessary coaching for managing GDM. It also reminds users to take medication if prescribed by their doctors. The app has six key features: blood glucose tracking, food logging, medication reminders, activity tracking, educational resources, and personalized recommendations. Overall, this work provides a valuable contribution to the field of mHealth-based maternal and child health in Bangladesh. However, further research is necessary to evaluate the effectiveness and feasibility of the intervention in real-world scenarios. Such evaluations will provide valuable insights into its potential impact and help refine the design to better serve the needs of GDM patients in Bangladesh. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： mHealth

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Equity and Fairness of Bayesian Knowledge Tracing

arXiv

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arXiv 2022年

作者： Tschiatschek, Sebastian Knobelsdorf, Maria Singla, Adish University of Vienna Faculty of Computer Science Research Network Data Science Vienna Austria Max-Planck Institute for Software Systems Saarbrücken Germany

We consider the equity and fairness of curricula derived from Knowledge Tracing models. We begin by defining a unifying notion of an equitable tutoring system as a system that achieves maximum possible knowledge in minimal time for each student interacting with it. Realizing perfect equity requires tutoring systems that can provide individualized curricula per student. In particular, we investigate the design of equitable tutoring systems that derive their curricula from Knowledge Tracing models. We first show that many existing models, including classical Bayesian Knowledge Tracing (BKT) and Deep Knowledge Tracing (DKT), and their derived curricula can fall short of achieving equitable tutoring. To overcome this issue, we then propose a novel model, Bayesian-Bayesian Knowledge Tracing (B2KT), that naturally enables online individualization and, thereby, more equitable tutoring. We demonstrate that curricula derived from our model are more effective and equitable than those derived from classical BKT models. Furthermore, we highlight that improving models with a focus on the fairness of next-step predictions might be insufficient to develop equitable tutoring systems. Copyright © 2022, The Authors. All rights reserved.

关键词： computer aided instruction

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Towards A Hybrid Quantum Differential Privacy

arXiv

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arXiv 2025年

作者： Song, Baobao Pokhrel, Shiva Raj Vasilakos, Athanasios V. Zhu, Tianqing Li, Gang The Faculty of Engineering and IT University of Technology Sydney Sydney Australia The Centre for Cyber Security Research and Innovation Deakin University Geelong Australia The Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia University of Agder Grimstad Norway The Faculty of Data Science City University of Macau China

Quantum computing offers unparalleled processing power but raises significant data privacy challenges. Quantum Differential Privacy (QDP) leverages inherent quantum noise to safeguard privacy, surpassing traditional DP. This paper develops comprehensive noise profiles, identifies noise types beneficial for QDP, and highlights the need for practical implementations beyond theoretical models. Existing QDP mechanisms, limited to single noise sources, fail to reflect the multi-source noise reality of quantum systems. We propose a resilient hybrid QDP mechanism utilizing channel and measurement noise, optimizing privacy budgets to balance privacy and utility. Additionally, we introduce Lifted Quantum Differential Privacy, offering enhanced randomness for improved privacy audits and quantum algorithm evaluation. © 2025, CC BY.

关键词： Quantum noise

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Efficient Convolutional Forward Modeling and Sparse Coding in Multichannel Imaging

arXiv

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arXiv 2024年

作者： Wang, Han Kvich, Yhonatan Pérez, Eduardo Römer, Florian Eldar, Yonina C. Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing Saarbrücken Germany Faculty of Math and Computer Science Weizmann Institute of Science Rehovot Israel Dept. Electronic Measurements and Signal Processing Technische Universität Ilmenau Ilmenau Germany

This study considers the Block-Toeplitz structural properties inherent in traditional multichannel forward model matrices, using Full Matrix Capture (FMC) in ultrasonic testing as a case study. We propose an analytical convolutional forward model that transforms reflectivity maps into FMC data. Our findings demonstrate that the convolutional model excels over its matrix-based counterpart in terms of computational efficiency and storage requirements. This accelerated forward modeling approach holds significant potential for various inverse problems, notably enhancing Sparse Signal Recovery (SSR) within the context LASSO regression, which facilitates efficient Convolutional Sparse Coding (CSC) algorithms. Additionally, we explore the integration of Convolutional Neural Networks (CNNs) for the forward model, employing deep unfolding to implement the Learned Block Convolutional ISTA (BC-LISTA). Copyright © 2024, The Authors. All rights reserved.

关键词： Ultrasonic testing

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From 5G to 6G: The Role of AI, Machine Learning, and Deep Learning in Wireless Systems

From 5G to 6G: The Role of AI, Machine Learning, and Deep Le...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Pradnyawant M. Gote Praveen Kumar Prateek Verma Prajyot Yesankar Adesh Pawar Saniya Saratkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science & Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible.

关键词： 6G mobile communication Wireless communication Deep learning Tactile Internet Technological innovation Sentiment analysis 5G mobile communication Reviews Heuristic algorithms Security

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Efficient Convolutional Forward Modeling and Sparse Coding in Multichannel Imaging

Efficient Convolutional Forward Modeling and Sparse Coding i...

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European Signal Processing Conference (EUSIPCO)

作者： Han Wang Yhonatan Kvich Eduardo Pérez Florian Römer Yonina C. Eldar Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing Saarbrücken Germany Dept. Electronic Measurements and Signal Processing Technische Universität Ilmenau Ilmenau Germany Faculty of Math and Computer Science Weizmann Institute of Science Rehovot Israel

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

关键词： Convolutional codes Adaptation models Convolution Computational modeling Signal processing algorithms Imaging Transforms Encoding Computational efficiency Sparse matrices

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Learning Fine-Grained Information Alignment for Calibrated Cross-Modal Retrieval

Learning Fine-Grained Information Alignment for Calibrated C...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jianhua Dong Shengrong Zhao Hu Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China

Masked Language Modeling (MLM) and Image-Text Matching (ITM) are always used in fusion encoder to learn the joint representation of images and text. In existing methods, the masking strategy of MLM leads to the neglect of image details during the modeling process. Meanwhile, the sampling strategy of ITM struggles to consistently select high-difficulty hard negative instances, reducing the effectiveness of constraints. This leads to challenges in aligning fine-grained information in cross-modal retrieval. In response to this challenge, a fine-grained information alignment-based visual language model (FAM) is proposed in this paper. On one hand, the attribute-based masking strategy is employed in MLM, helping the model focus on the details of objects in images during modeling. On the other hand, the robust hard negative sample generation strategy provides challenging negative samples for ITM by altering the relationships between objects. This enables the model to align relationships between objects in different modalities and thus calibrates cross-modal retrieval. Extensive experiments demonstrate the effectiveness of the model in cross-modal retrieval tasks.

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