检索结果-内蒙古大学图书馆

Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Vardhan, Harsh Hyde, David Timalsina, Umesh Volgyesi, Peter Sztipanovits, Janos Department of Computer Science Vanderbilt University 1400 18th Ave. S. NashvilleTN37212-2846 United States Institute for Software Integrated Systems Vanderbilt University 1025 16th Ave. S. NashvilleTN37212-2328 United States Department of Electrical and Computer Engineering Vanderbilt University 400 24th Ave. S. NashvilleTN37212 United States

Physics simulations like computational fluid dynamics (CFD) are a computational bottleneck in computer-aided design (CAD) optimization processes. To overcome this bottleneck, one requires either an optimization framework that is highly sample-efficient, or a fast data-driven proxy (surrogate model) for long-running simulations. Both approaches have benefits and limitations. Bayesian optimization is often used for sample efficiency, but it solves one specific problem and struggles with transferability;alternatively, surrogate models can offer fast and often more generalizable solutions for CFD problems, but gathering data for and training such models can be computationally demanding. In this work, we leverage recent advances in optimization and artificial intelligence (AI) to explore both of these potential approaches, in the context of designing an optimal unmanned underwater vehicle (UUV) hull. For first approach, we investigate and compare the sample efficiency and convergence behavior of different optimization techniques with a standard CFD solver in the optimization loop. For the second approach, we develop a deep neural network (DNN) based surrogate model to approximate drag forces that would otherwise be computed via the CFD solver. The surrogate model is in turn used in the optimization loop of the hull design. Our study finds that the Bayesian Optimization—Lower Condition Bound (BO-LCB) algorithm is the most sample-efficient optimization framework and has the best convergence behavior of those considered. Subsequently, we show that our DNN-based surrogate model predicts drag force on test data in tight agreement with CFD simulations, with a mean absolute percentage error (MAPE) of 1.85%. Combining these results, we demonstrate a two-orders-of-magnitude speedup (with comparable accuracy) for the design optimization process when the surrogate model is used. To our knowledge, this is the first study applying Bayesian optimization and DNN-based surrogate modelin

关键词： Computational fluid dynamics

HURWITZ SERIES RINGS SATISFYING A ZERO DIVISOR PROPERTY

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Mosallaei, Behrooz Afrouzmehr, Moein Abshari, Danial Farivar, Sepideh The Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM88003 United States Department of Economics Applied Statistics and International Business New Mexico State University Las CrucesNM88003 United States Software and Information Systems Department University of North Carolina at Charlotte CharlotteNC28223 United States Department of Computer Science University of Nevada Las Vegas 4505 S. Maryland Parkway Las VegasNV89154-4030 United States

In this paper, we study zero divisors in the Hurwitz series rings and the Hurwitz polynomial rings over general non-commutative rings. We first construct Armendariz rings that are not Armendariz of the Hurwitz series type and find various properties of (the Hurwitz series) Armendariz rings. We show that for a semiprime Armendariz of the Hurwitz series type (so reduced) ring R with a.c.c. on annihilator ideals, HR (the Hurwitz series ring with coefficients over R) has finitely many minimal prime ideals, say B1, . . ., Bm such that B1 · . . . · Bm = 0 and Bi = HAi for some minimal prime ideal Ai of R for all i, where A1, . . ., Am are all minimal prime ideals of R. Additionally, we construct various types of (the Hurwitz series) Armendariz rings and demonstrate that the polynomial ring extension preserves the Armendarizness of the Hurwitz series as the Armendarizness. © 2023, CC0.

关键词： Algebra

Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication

学校读者我要写书评

暂无评论

Towards Cluster-Based Split Federated Learning Approach for ...

Cyber Security in Networking Conference (CSNet)

作者： Mohamad Wazzeh Mohamad Arafeh Hakima Ould-Slimane Chamseddine Talhi Azzam Mourad Hadi Otrok Department of Software and IT Engineering Ecole de Technologie Supérieure (ÉTS) Montreal Canada Department of Mathematics and Computer Science Université du Québec à Trois-Riviéres (UQTR) Trois-Rivières Canada Department of CSM Cyber Security Systems and Applied AI Research Center Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security.

关键词：

Implicit Multi-Scale Swin Transformer Network for Image Denoising

学校读者我要写书评

暂无评论

IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Qi Ding, Yuwei Zhang, Weiqi Zhu, Yian Zhang, Bob Lin, Jerry Chun-Wei Harbin Institute of Technology at Weihai School of Economics and Management Weihai China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Northwestern Polytechnical University School of Computer Science Xi’an China Northwestern Polytechnical University Yangtze River Delta Research Institute Taicang China University of Macau PAMI Research Group Department of Computer and Information Science Taipa China Department of Distributed Systems and IT Devices Electrical Engineering and Mathematical Sciences Silesian university of technology Poland

Image denoising has been used in various edge computing scenarios such as consumer electronics to improve the image quality and user experience. Existing image denoising methods based on Convolutional Neural Networks (CNNs) and vision Transformers achieve good performance by empirically utilizing residual neural network (ResNet) as basic component. However, ResNet lacks interpretability in network design and also long-term memory of features, potentially restricting the performance of denoising networks. In this paper, we propose an Implicit Multi-scale Swin Transformer Network (IMSNet) for image denoising, which introduces the implicit Euler scheme from the feature memory perspective. Specifically, densely connected implicit feature extraction blocks (IFEBs) are designed to learn the residual mapping between noisy and clean images. The IFEB reformulates the initial skip connection of ResNet based on the implicit Euler discretization, providing both network interpretability and long-term memory. In IFEB, the multi-scale swin Transformer block (MSB) is designed as the implicit layer to capture spatial details and non-local contextual information at different scales. Additionally, a cross-layer feature fusion block (CLFF) is proposed to further improve feature reuse capabilities. Compared to existing denoising networks, the extensive experiments demonstrate the superior performance of our IMSNet in various image denoising tasks, and the flexibility in the practical applications with proposed light model on resource-restricted platforms such as consumer electronic devices. © 1975-2011 IEEE.

关键词： Image denoising

Gender-Wise Perception of Students Towards Blended Learning in Higher Education: Pakistan

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Soomro, Saira Soomro, Arjumand Bano Bhatti, Tarique Gulzar, Yonis Department of Distance Continuing and Computer Education Faculty of Education University of Sindh Hyderabad Pakistan Department of Software Engineering Faculty of Engineering and Technology University of Sindh Sindh Jamshoro Pakistan Department of Psychological Testing Guidance & Research Faculty of Education University of Sindh Hyderabad Pakistan Department of Management Information Systems College of Business Administration King Faisal University Al Ahsa Saudi Arabia

Blended learning (BL) is a recent trend among many options that can best fit learners' needs, regardless of time and place. This study aimed to discover students' perceptions of BL and the challenges faced by them while using technology. This quantitative study used data gathered from 300 students enrolled in four public universities in the Sindh province of Pakistan. The findings show that students were comfortable with the use of technology, and it has a positive effect on their academic experience. The study also showed that the use of technology encourages peer collaboration. The challenges found include: neither teacher support nor a training programme was provided to the students for the courses which needed to shift from a traditional face to face paradigm to a blended format, a lack of specialised skills in laboratory assistants for the courses with a blended format and a shortage of hi-tech computer laboratories/computer units to run these courses. Therefore, it is recommended that authorities must develop and incorporate a comprehensive mechanism for the effective implementation of BL in the teaching-learning process. Heads of departments should also provide additional computing infrastructure to their departments. © 2022, CC BY-NC-ND.

关键词： Students

Emerging case oriented agents for sustaining educational institutions going green towards environmental responsibility

学校读者我要写书评

暂无评论

Journal of systems and Information Technology 2019年第2期21卷 186-214页

作者： Anthony Jnr, Bokolo Abdul Majid, Mazlina Romli, Awanis Department of Computer Science Norwegian University of Science and Technology NTNU Trondheim Norway Universiti Malaysia Pahang Faculty of Computer Systems and Software Engineering Kuantan Malaysia

Purpose: The purpose of this paper is to design a system deployment model that integrates case-based agent technique to develop an eco-responsibility decision support tool for greening educational institutions toward environmental responsibility. Design/methodology/approach: Data were collected through questionnaires distributed among a statistical population that comprised practitioners across educational institutions in Malaysia that implement green practices. The questionnaire measured the feasibility of the developed tool based on factors derived from the literature. Accordingly, descriptive, exploratory and factor analysis approach using statistical package for social sciences (SPSS) was used to test the feasibility of the developed tool. Findings: Results from descriptive analysis confirm the tool is feasible based on mean values that range from 4.1619 to 3.6508 on a five-point scale, indicating that the tool is effective in sustaining educational institutions going green. Besides, results from exploratory analysis verify the reliability of the tool based on the acceptable Cronbach’s alpha reliability coefficient score higher than 0.7 and Kaiser–Meyer–Olkin value being above 0.5. Finally, results from factor analysis reveal that the developed tool is usable, efficient, helpful, flexible and credible and supports educational institutions in going green at 88.44 per cent of the total variance, suggesting that the respondents are satisfied with the tool. Research limitations/implications: The sample population in this study comprises only practitioners from educational institutions in Malaysia. Theoretically, this research provides feasibility factors and associated items that can be used in evaluating developed information systems. Practical implications: Practically, this study develops an eco-responsibility decision support tool to facilitate green strategies and provides information on how practitioners in educational institutions can improve green growth. So

关键词： Sustainable development

Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wehbi, Osama Arisdakessian, Sarhad Guizani, Mohsen Wahab, Omar Abdel Mourad, Azzam Otrok, Hadi Khzaimi, Hoda Al Ouni, Bassem Mohammad Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Computer and Software Engineering Polytechnique Montréal MontrealQC Canada Artificial Intelligence & Cyber Systems Research Center Department of CSM Lebanese American University Lebanon KU 6G Research Center Department of CS Khalifa University United Arab Emirates Khalifa University Abu Dhabi United Arab Emirates Division of Engineering New York University Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates

Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random client selection technique, poses several threats to the system’s integrity due to the possibility of malicious client selection. Primarily, the existing literature focuses on assessing the trustworthiness of clients, neglecting the crucial aspect of trust in federated servers. To bridge this gap, in this work, we propose a novel framework that addresses the mutual trustworthiness in federated learning by considering the trust needs of both the client and the server. Our approach entails: (1) Creating preference functions for servers and clients, allowing them to rank each other based on trust scores, (2) Establishing a reputation-based recommendation system leveraging multiple clients to assess newly connected servers, (3) Assigning credibility scores to recommending devices for better server trustworthiness measurement, (4) Developing a trust assessment mechanism for smart devices using a statistical Interquartile Range (IQR) method, (5) Designing intelligent matching algorithms considering the preferences of both parties. Based on simulation and experimental results, our approach outperforms baseline methods by increasing trust levels, global model accuracy, and reducing non-trustworthy clients in the system. © 2024, CC BY-NC-SA.

关键词： Recommender systems

Any-to-Any Vision-Language Model for Multimodal X-ray Imaging and Radiological Report Generation

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Molino, Daniele di Feola, Francesco Shen, Linlin Soda, Paolo Guarrasi, Valerio Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Department of Diagnostics and Intervention Biomedical Engineering and Radiation Physics Umeå University Umeå Sweden College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Generative models have revolutionized Artificial Intelligence (AI), particularly in multimodal applications. However, adapting these models to the medical domain poses unique challenges due to the complexity of medical data and the stringent need for clinical accuracy. In this work, we introduce a framework specifically designed for multimodal medical data generation. By enabling the generation of multi-view chest X-rays and their associated clinical report, it bridges the gap between general-purpose vision-language models and the specialized requirements of healthcare. Leveraging the MIMIC-CXR dataset, the proposed framework shows superior performance in generating high-fidelity images and semantically coherent reports. Our quantitative evaluation reveals significant results in terms of FID and BLEU scores, showcasing the quality of the generated data. Notably, our framework achieves comparable or even superior performance compared to real data on downstream disease classification tasks, underlining its potential as a tool for medical research and diagnostics. This study highlights the importance of domain-specific adaptations in enhancing the relevance and utility of generative models for clinical applications, paving the way for future advancements in synthetic multimodal medical data generation. © 2025, CC BY-NC-ND.

关键词：

Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J.P.C. School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Electrical and Information Engineering Zhengzhou University the Henan International Joint Laboratory of Intelligent Health Information System The National Telemedicine Center The National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom The Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of School of Software Nanjing University of Information Science and Technology Nanjing210044 China Amazonas State University AM Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC systems. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU’s utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 92.11%, increasing the final user utility function value by 0.24%, and decreasing the variance of results by 95.45%. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)