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arXiv 2024年

作者： Umair, Muhammad Hashmi, Syed Jalaluddin Lee, Young-Koo Department of Computer Science and Engineering Kyung Hee University Korea Republic of College of Software Kyung Hee University Korea Republic of

Keyphrase ranking plays a crucial role in information retrieval and summarization by indexing and retrieving relevant information efficiently. Advances in natural language processing, especially large language models (LLMs), have improved keyphrase extraction and ranking. However, traditional methods often overlook diversity, resulting in redundant keyphrases. We propose a novel approach using Submodular Function Optimization (SFO) to balance relevance and diversity in keyphrase ranking. By framing the task as submodular maximization, our method selects diverse and representative keyphrases. Experiments on benchmark datasets show that our approach outperforms existing methods in both relevance and diversity metrics, achieving SOTA performance in execution time. Our code is available online. © 2024, CC BY.

关键词： Information retrieval

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Versatile Test Reactor Open Digital engineering Ecosystem

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Insight 2025年第1期28卷 64-68页

作者： Ritter, Christopher Browning, Jeren Suyderhoud, Peter Hays, Ross Marshall, AnnMarie Han, Kevin Ashbocker, Taylor Darrington, John Nelson, Lee Idaho National Laboratory Department of Digital Systems & Engineering Versatile Test Reactor Program Idaho FallsID83401 United States Pivotal Solutions 3 Grace Avenue Suite 162 Great NeckNY11021 United States North Carolina State University Department of Civil and Environmental Engineering United States Department of Computer Science University of Illinois at Urbana-Champaign 205 N. Mathews Ave. UrbanaIL61801 United States

Modern design of nuclear facilities represents unique challenges: enabling the design of complex advanced concepts, supporting geographically dispersed teams, and supporting first-of-a-kind system development. Errors made early in design can introduce silent errors. These errors can cascade causing unknown risk of complex engineering programs. The Versatile Test Reactor (VTR) Program uses digital-engineering principles for design, procurement, construction, and operation to reduce risk and improve efficiencies. Digital engineering is an integrated, model-based approach which connects proven digital tools such as building information management (BIM), project controls, and systems-engineering software tools into a cohesive environment. The VTR team hypothesizes using these principals can lead to similar risk and cost reductions and schedule efficiencies observed in other engineering industries. This research investigates the use of a digital engineering ecosystem in the design of a 300-MWt sodium-cooled fast reactor. This ecosystem was deployed to over 200 engineers and used to deliver the conceptual design of the VTR. We conclude that initial results show significant reductions in user latency (1000x at peak use), the possibility of direct finite-element-analysis (FEA) integrations to computer-aided design (CAD) tools, and nuclear reactor system design descriptions (SDDs) that we can fully link throughout design in data-driven requirements-management software. These early results led to the VTR maintaining milestone performance during the COVID-19 pandemic. Copyright © 2022 by Idaho National laboratory. Published by INCOSE with permission.

关键词： Sodium-cooled fast reactors

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Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

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computers, Materials & Continua 2022年第6期71卷 5837-5854页

作者： El-Sayed M.El-Kenawy Abdelhameed Ibrahim Nadjem Bailek Kada Bouchouicha Muhammed A.Hassan Basharat Jamil Nadhir Al-Ansari Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansouraEgypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of Tamanghasset10034TamanghassetAlgeria URERMS Centre de Developpement des Energies Renouvelables(CDER)01000AdrarAlgeria Mechanical Power Engineering Department Faculty of EngineeringCairo UniversityGiza12613GizaEgypt Department Computer Sciences Universidad Rey Juan CarlosMostoles28933MadridSpain Department of Civil Environmental and Natural Resources EngineeringLulea University of Technology97187LuleaSweden

In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in ***-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two *** computational algorithms were considered and analyzed for the suitability of *** two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization *** accuracy of the developed models was analyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA *** the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good ***,the newly proposed ensemble algorithms exhibited even better *** proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than *** was further verified by benchmarking the new ensemble against several popular swarm intelligence *** is concluded that the proposed algorithms are far superior to the commonly adopted ones.

关键词： Renewable energy resources hybrid modeling tilted solar irradiation arid region

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Distributed Feature Reduction Signal Sorting Algorithm

Distributed Feature Reduction Signal Sorting Algorithm

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Wan, Liangtian Wang, Jinjing Sun, Lu Wang, Xianpeng Lu, Cunbo Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian116026 China School of Information and Communication Engineering Hainan University Haikou570228 China Information Science Academy China Electronics Technology Group Corporation Beijing100098 China

ISBN: (纸本)9789811966125

With the development of science and technology, modern war has changed from traditional mechanized war to electronic war. Electronic warfare has become the mainstream combat mode in modern war. At present, the electromagnetic environment is very chaotic and complex, the pulse density increases rapidly, and the pulse modulation mode is complex and changeable. In order to deal with the low efficiency of traditional methods in complex electromagnetic environment, a distributed feature reduction signal sorting method is proposed. Firstly, attribute entropy regularization is added to the objective function, and different weights are given to different features. Secondly, the feature reduction process is added to select which dimension of features to be discarded by setting the threshold. Each iteration of the algorithm will update the membership matrix, cluster center and feature weight matrix until the termination conditions is satisfied. Simulation results show that the algorithm can reduce the number of iterations and time, and improve the accuracy of radar signal sorting results by reducing the number of features. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reduction

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FedSSP: federated graph learning with spectral knowledge and personalized preference 24

FedSSP: federated graph learning with spectral knowledge and...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zihan Tan Guancheng Wan Wenke Huang Mang Ye National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China and Taikang Center for Life and Medical Sciences Wuhan University Wuhan China

ISBN: (纸本)9798331314385

Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In cross-domain scenarios, structural heterogeneity poses significant challenges for pFGL. Nevertheless, previous pFGL methods incorrectly share non-generic knowledge globally and fail to tailor personalized solutions locally under domain structural shift. We innovatively reveal that the spectral nature of graphs can well reflect inherent domain structural shifts. Correspondingly, our method overcomes it by sharing generic spectral knowledge. Moreover, we indicate the biased message-passing schemes for graph structures and propose the personalized preference module. Combining both strategies, we propose our pFGL framework FedSSP which Shares generic Spectral knowledge while satisfying graph Preferences. Furthermore, We perform extensive experiments on cross-dataset and cross-domain settings to demonstrate the superiority of our framework. The code is available at https://***/OakleyTan/FedSSP.

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Design and Implementation of a Web-Based Application for Managing Agricultural Health Products

Design and Implementation of a Web-Based Application for Man...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Olugbenga O. Oluwagbemi Emebo Onyeka Ikashi S. Ishieka Anna Wieclaw Ambrose A. Azeta Department of Computer Science Middlesex University London United Kingdom Department of Computer Science Virginia Tech University Blacksburg United States of America Department of Software Engineering Namibia University of Science and Technology Windhoek Namibia

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Caring for the health of food crops and plants can help boost food production and eradicate hunger according to the second United Nations (UN) Sustainable Development Goal (SDG). The procurement of agricultural health products such as fertilizers, fungicides, pesticides, and herbicides are a major problem for an average Nigerian farmer. This results in low crop yield and negatively impacts on food production which eventually causes hunger in the land. Existing methods of procuring these health products have majorly been through offline platforms such as agricultural stores and other relevant stores. This has its disadvantages such as inaccessible roads to stores where such products can be obtained, varying prices, health products out of stock, ignorance in the application of such products. These barriers deter farmers from procuring these health products. In this paper, a survey was conducted on the needs and problems of farmers. The results of the survey informed the design and implementation of an agricultural health Informatics web application for health product management for food crops. The application was designed using unified modelling language class diagram and implemented using Python. SQLite3 serves as the backend database. The agricultural informatics solution will help farmers in Nigeria to easily procure health products for food crops and get the products delivered without stress and worry when fully deployed. This will boost food production, thus eradicating hunger; it will also help in exporting food crops to other countries.

关键词： Procurement Surveys Food manufacturing Roads Unified modeling language Transforms Pesticides Bioinformatics Sustainable development Stress

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Navigating the Future: Artificial Intelligence and Sustainable Transportation in Morocco 9th

Navigating the Future: Artificial Intelligence and Sustainab...

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9th International Conference on Smart City Applications, SCA 2024

作者： Chegri, B.E. Rhajbal, Z. El Mettiti, A. Berhich, A. Saadane, R. Charif, M. Chehri, A. Mohammed V University in Rabat Rabat Morocco LRIT-GSCM Faculty of Sciences University Mohamed V Rabat Rabat Morocco AMIPS Research Team Computer Science Department EMI Mohammed V University in Rabat Rabat Morocco Laboratory Engineering System SIRC/LAGeS EHTP Casablanca Morocco Department of Mathematics and Computer Science Royal Military College Kingston Canada

ISBN: (纸本)9783031886522

The integration of Artificial Intelligence (AI) into sustainable mobility represents a transformative paradigm for transportation systems, particularly within the unique socioeconomic landscape of Morocco. This study delves into the dynamic intersection of AI and sustainable mobility, examining its implications for enhancing efficiency, reducing environmental impact, and improving overall accessibility in Moroccan transportation. Against the backdrop of Morocco’s commitment to sustainable development, the research navigates through the intricate web of challenges and opportunities posed by the fusion of AI technologies and mobility solutions. Through a comprehensive literature review, case studies, and recommendations, this article provides a roadmap for policymakers, researchers, and stakeholders to harness the potential of AI in shaping a more resilient and technologically advanced transportation infrastructure. With a focus on both global best practices and Morocco’s specific needs, the study not only underscores the benefits of AI integration but also addresses ethical considerations and potential socio-cultural implications. As Morocco advances towards a sustainable future, the synergies between AI and mobility emerge as a pivotal force in steering transportation towards efficiency, accessibility, and environmental responsibility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Autonomous vehicles

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Mechanism of Threshold Voltage Instability in Double Gate α-IGZO Nanosheet TFT under Bias and Temperature Stress

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IEEE Journal of the Electron Devices Society 2024年 12卷 464-471页

作者： Aslam, Muhammad Chang, Shu-Wei Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Lee, Wen-Hsi Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan City701401 Taiwan Institute of Pioneer Semiconductor Innovation Industry Academic Innovation School National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Amorphous indium gallium zinc oxide (a-IGZO)-based thin film transistors (TFTs) are increasingly becoming popular because of their potential in futuristic applications, including CMOS technology. Given the demand for CMOS-compatible, ultra-scaled, reliable, and high-performing devices, we fabricate and analyze scaled-channel a-IGZO-TFTs with an optimal double-gate structure, a thin nanosheet-based channel, and an effective high- κ dielectric namely HfO2. The reliably reported double gate IGZO nanosheet TFTs (DG-IGZO-NS-TFTs) are tested under positive and negative bias stress at variant temperatures, and the resulting modulations are analyzed critically. The reported DG-IGZO-NSTFTs exhibit a negative side threshold voltage shift (Δ Vth) accompanied by an increase in Ion/Ion(0) under negative bias temperature stress (NBTS) at elevated temperatures, which indicates the presence of additional charges. An anomalous negative side shifting of the Vth is observed under positive bias temperature stress (PBTS), where diffused hydrogen atoms are identified as introducing excess n-type carriers into the channel and causing the observed Δ Vth. The spectroscopic analysis is performed to establish evidence for the assumed mechanisms, and the role of individual gates is investigated in the context of performance variance under temperature-bias stress. Moreover, the partial reversibility of the stress-induced degradation is experimentally established and methodically discussed. Overall, the reported results offer a comprehensive understanding of scaled-channel DG-NS-IGZO-TFTs, which help shape performance-enhancement strategies, control degradation mechanisms, and define appropriate application scenarios. © 2013 IEEE.

关键词： Thin film transistors

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Estimating Chlorophyll Content from Hyperspectral Data Using Gradient Features 23rd

Estimating Chlorophyll Content from Hyperspectral Data Usin...

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23rd International Conference on Computational science, ICCS 2023

作者： Ruszczak, Bogdan Wijata, Agata M. Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Opole Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Gliwice Poland KP Labs Gliwice Poland

ISBN: (纸本)9783031360206

Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high-dimensional, therefore it is challenging to transfer from the imaging device, store and investigate. We propose a machine learning pipeline for estimating chlorophyll content from hyperspectral data. It benefits from the Savitzky-Golay filtering to smooth the (potentially noisy) spectral curves, and from gradient-based features extracted from such a smoothed signal. The experiments revealed that our approach significantly outperforms the state of the art according to the widely-established estimation quality metrics obtained for four chlorophyll-related parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Enhancing Energy Efficiency in WSNs With Hybrid LEACH-D and ANN

Enhancing Energy Efficiency in WSNs With Hybrid LEACH-D and ...

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Multimedia University engineering Conference (MECON)

作者： Muhammad Salman Qamar Ihsan ul Haq Muhammad Fahad Munir Mardeni Bin Roslee Adnan Anwar Awan Athar Waseem Department of Electrical and Computer Engineering International Islamic University Islamabad Pakistan Centre for Wireless Technology Faculty of Engineering Multimedia University Cyberjaya Malaysia Department of Computer Science and Software Engineering School of Engineering California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798331530747

ISBN: (纸本)9798331530754

This paper explores strategies to improve the energy efficiency and operational longevity of Wireless Sensor Networks (WSNs) through the optimization of clustering techniques and energy efficient routing protocols. The proposed methodology is precisely crafted to optimize energy consumption (EC) while preserving the overall effectiveness of WSNs. LEACH (Low Energy Adaptive Cluster Hierarchy) is a cluster-based protocol where non-cluster head nodes deactivate their RF until their allocated time slots. Nevertheless, a drawback of traditional LEACH is its random rotation of local Cluster Heads (CH), leading to unequal cluster distribution. To overcome this issue, we proposed a novel hybrid technique called LEACH-D protocol and Artificial Neural Network (ANN). The LEACH-D algorithm enhances the duration of transmission tasks while ensuring uniform battery energy consumption. On the other hand, the ANN plays a pivotal role in coordinating optimal routing decisions and CH placement, refining data aggregation and transmission mechanisms to restrain idle listening. The findings indicate that the suggested approach manifests as substantial reductions in energy consumption and prolonged network lifespan of WSN.

关键词： Wireless sensor networks Energy consumption Simulation Artificial neural networks Data aggregation Energy efficiency Magnetic heads Routing protocols Batteries Optimization

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