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A Graph-Assisted Digital-Twin-Driven Multiagent Shared Offloading for Internet of Vehicles

IEEE Internet of Things Journal 2025年第11期12卷 17349-17363页

作者： Zahangir Alam, Md Rahman, Suryaia Asif Bin Khaled, Md Islam, Ashraful Jamalipour, Abbas Independent University Bangladesh Department of Computer Science and Engineering Center for Computational and Data Sciences Dhaka1229 Bangladesh The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Vehicular edge computing (VEC) allows vehicles to process part of the tasks locally at the network edge while offloading the rest of the tasks to a centralized cloud server for processing. A massive volume of tasks generated by the Internet of Vehicles (IoV) leads to buffer overflow that causes higher latency. Elevating latency, in turn, can increase network energy consumption. Both higher latency and energy consumption lead to a degradation of network performance. Therefore, VEC design requires a balance between latency and energy consumption tradeoff. To reduce overwhelming amount of offloading to edge servers, a cooperative cluster-based shared offloading strategy has been proposed in this work. We use digital twin technology in VEC for managing and adapting to environmental dynamic changes. Then, we leverage Lyapunov (Ly) optimization to transform the stochastic offloading problem into a more manageable deterministic form. Finally, we present a decentralized coordination graph (CG)-driven Ly-based multiagent deep deterministic policy gradient (CG-LyMADDPG) algorithm that trains agents toward energy efficient optimal offloading policy while maintaining queue stability at a maximum delay constraint. The experimental result shows that the proposed learning significantly outperforms the baseline algorithms for energy savings while maintain queue stability. © 2014 IEEE.

关键词： Stochastic systems

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Machine Learning Framework with Wilson Loop Perceptron for Measuring the Rate of Mutation Patterns between DNA and RNA Viruses

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SN computer science 2025年第2期6卷 1-20页

作者： Fongngen, Weerasak Kanjamapornkul, Kabin Petharn, Surapong Department of Computer Science Faculty of Science Lampang Rajabhat University Lampang 52100 Thailand Department of Chemistry Faculty of Science Center of Excellence in Computational Chemistry (CECC) Chulalongkorn University Bangkok 10330 Thailand

The Wilson loop is indicative of the pathway encompassed within the viral replication process, which carries the coherent gauge field behavior present in the genetic coding of the virus. We enhance the capabilities of the support spinor machine by integrating supplementary attributes through the incorporation of the knot and link characteristics of the Wilson loop, as derived from biological contexts. This framework was employed to get into more deeper understanding of the mutational dynamics within viral replication, with particular emphasis on the concurrent quantum genotyping transfer along the continuous gauge field’s geodesic evolutionary trajectory. This trajectory links the genetic code existing in viral RNA and protein. Furthermore, a comparative analysis was undertaken to quantifying the mutation patterns in the replication loop of the evolutionary pathway. This was achieved by utilizing the spectrum of tensor correlation between the Marburg virus and the Monkeypox virus. The computational results for the mutation rates of the Marburg virus, 2.31×10-4 and the Monkeypox virus, 9.61×10-6, align with the biological hypothesis that RNA viruses mutate at a faster rate than DNA viruses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Marburg virus Quantum genotyping Support vector machine Wilson loop

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Directional Prediction of Financial Time Series Using SVM and Wilson Loop Perceptron

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SN computer science 2024年第4期5卷 395页

作者： Srisuay, Samai Kanjamapornkul, Kabin Fongngen, Weerasak Department of Computer Science Faculty of Science Lampang Rajabhat University Lampang 52100 Thailand Center of Excellence in Computational Chemistry (CECC) Department of Chemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand

The Wilson loop is indicative of the pathway encompassed within the market cocycle, which carries the coherent gauge field behavior present in the financial time series data. We enhance the capabilities of the support vector machine by integrating supplementary attributes through the incorporation of the knot and link characteristics of the Wilson loop, as derived from market microstructure contexts. This framework was employed to capture the financial market dynamics within time series data, with particular emphasis on the parallel transport of co-state between predictor and predictant transfer along the continuous behavior field’s predictive evolutionary lift path trajectory. We found that the average performance of Wilson loop perceptron in empirical analysis of a sample set of closed price of DE30 and EUR/USD exchange rate is 68.42% when compared with SVM which have 49.17%. Furthermore, we conclude from data analysis of statistical DM-test that has Wilson loop perceptron better performance than SVM in directional prediction of intraday financial time series. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Financial time series Machine learning Prediction Wilson loop

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SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

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computers, Materials & Continua 2023年第5期75卷 2383-2396页

作者： Abida Sharif Muhammad Imran Sharif Muhammad Attique Khan Nisar Ali Abdullah Alqahtani Majed Alhaisoni Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Department of Computer Science HITEC University TaxilaPakistan College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based ***,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network *** IoVbased content distribution is an emerging concept in which all the vehicles are connected via the *** to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new *** this,a novel strategy mediator framework is presented in this paper for managing the network resources ***-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the *** to the limited memory of edge devices,the delectable bloom filters are used for caching and ***,the proposed scheme is compared with the existing variants for validating its effectiveness.

关键词： Internet of vehicles(IOV) content dissemination multi mediator data traffic management

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Quantifying the weakness of ties with hierarchy-based link centrality

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science China(Information sciences) 2025年

作者： Jianhong MOU Longyun WANG Kang WEN Bitao DAI Suoyi TAN Fredrik LILJEROS Petter HOLME Xin LU College of Systems Engineering National University of Defense Technology Department of Sociology Stockholm University Department of Computer Science Aalto University Center for Computational Social Science Kobe University

Quantifying the significance of ties in preserving network connectivity is crucial for identifying weak ties, which often serve as bridges between communities, and for detecting community structures. However, accurately characterizing network connectivity and formalizing the relationship between weak ties and communities remain challenging. In this study, we introduce hierarchy-based link centrality(HLC), a novel metric based on the dissimilarity between the original network and its contracted version, where the terminal nodes of links merge and connect to all their neighbors. This dissimilarity is quantified by variations in the network hierarchy, specifically the nodal distance distributions. In addition to the experiments on weak tie identification and link-based network disintegration, we develop a link-based community detection(LCD) approach that focuses on optimal link ranking to elucidate community structures. Experiments across various networks demonstrate that HLC excels in identifying weak ties, achieving a 2.9% higher accuracy than the second-best metric. It also outperforms others in detecting critical link combinations for network disintegration, reducing the average size of the giant connected component by 7.2% compared to the suboptimal counterpart. Furthermore, HLC enhances community detection, achieving optimal partitioning with an average 5.7% improvement in modularity over five other indices. These results highlight the effectiveness of HLC in quantifying weak ties and suggest broad applications for this innovative approach in network analysis.

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PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

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Journal of Systems Engineering and Electronics 2024年第4期35卷 965-975页

作者： JIANG Haipeng WU Guoqing SUN Mengdan LI Feng SUN Yunfei FANG Wei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and TechnologyJiangnan UniversityWuxi 214122China China Ship Scientific Research Center Wuxi 214082China Taihu Laboratory of Deepsea Technological Science Wuxi 214082China Department of Mathematics Nanjing UniversityNanjing 210023China

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search *** have good running and mining performance,but they still require huge computational resource and may miss many *** to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded *** show that the mining performance of PHUI-GA outperforms the existing *** mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

关键词： high utility itemset mining(HUIM) graphics process-ing unit(GPU)parallel genetic algorithm(GA) mining perfor-mance

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An IoT Environment Based Framework for Intelligent Intrusion Detection

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computers, Materials & Continua 2023年第5期75卷 2365-2381页

作者： Hamza Safwan Zeshan Iqbal Rashid Amin Muhammad Attique Khan Majed Alhaisoni Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Deparment of Computer Science UET TaxilaTaxila47080Pakistan Department of Computer Science HITEC UniversityTaxila47080Pakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Software-defined networking(SDN)represents a paradigm shift in network traffic *** distinguishes between the data and control *** are then used to communicate between these *** controller is central to the management of an SDN network and is subject to security *** research shows how a deep learning algorithm can detect intrusions in SDN-based IoT ***,low accuracy,and efficient feature selection is all *** propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory(LSTM).In this study,a new dataset based specifically on Software Defined Networks is used in *** obtain the best and most relevant features,a feature selection technique is *** experiments have revealed that the proposed solution is a superior method for detecting flow-based *** performance of our proposed model is also measured in terms of accuracy,recall,and precision.F1 rating and detection time Furthermore,a lightweight model for training is proposed,which selects fewer features while maintaining the model’s *** show that the adopted methodology outperforms existing models.

关键词： Software-defined networks prediction modeling machine learning deep learning

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On the Scalability of Computing Genomic Diversity Using SparkLeBLAST: A Feasibility Study

On the Scalability of Computing Genomic Diversity Using Spar...

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2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Prabhu, Ritvik Moussad, Bernard Youssef, Karim Vatai, Emil Feng, Wu-Chun Virginia Tech Department of Computer Science United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Riken Center for Computational Science Japan

ISBN: (纸本)9798350387131

Studying the genomic diversity of viruses can help us understand how viruses evolve and how that evolution can impact human health. Rather than use a laborious and tedious wet-lab approach to conduct a genomic diversity study, we take a computational approach, using the ubiquitous NCBI BLAST and our parallel and distributed SparkLeBLAST, across 53 patients (40,000,000 query sequences) on Fugaku, the world's fastest homogeneous supercomputer with 158,976 nodes, where each code contains a 48-core A64FX processor and 32 GB RAM. To project how long BLAST and SparkLeBLAST would take to complete a genomic diversity study of COVID-19, we first perform a feasibility study on a subset of 50 query sequences from a single COVID-19 patient to identify bottlenecks in sequence alignment processing. We then create a model using Amdahl's law to project the run times of NCBI BLAST and SparkLeBLAST on supercomputing systems like Fugaku. Based on the data from this 50-sequence feasibility study, our model predicts that NCBI BLAST, when running on all the cores of the Fugaku supercomputer, would take approximately 26.7 years to complete the full-scale study. In contrast, SparkLeBLAST, using both our query and database segmentation, would reduce the execution time to 0.026 years (i.e., 22.9 hours) - resulting in more than a 10,000× speedup over using the ubiquitous NCBI BLAST. © 2024 IEEE.

关键词： COVID-19

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CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM 38

CryoBench: Diverse and challenging datasets for the heteroge...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jeon, Minkyu Raghu, Rishwanth Astore, Miro Woollard, Geoffrey Feathers, Ryan Kaz, Alkin Hanson, Sonya M. Cossio, Pilar Zhong, Ellen D. Department of Computer Science Princeton University PrincetonNJ United States Center for Computational Biology Flatiron Institute New YorkNY United States Center for Computational Mathematics Flatiron Institute New YorkNY United States Department of Computer Science University of British Columbia VancouverBC Canada

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining high-resolution 3D biomolecular structures from imaging data. Its unique ability to capture structural variability has spurred the development of heterogeneous reconstruction algorithms that can infer distributions of 3D structures from noisy, unlabeled imaging data. Despite the growing number of advanced methods, progress in the field is hindered by the lack of standardized benchmarks with ground truth information and reliable validation metrics. Here, we introduce CryoBench, a suite of datasets, metrics, and benchmarks for heterogeneous reconstruction in cryo-EM. CryoBench includes five datasets representing different sources of heterogeneity and degrees of difficulty. These include conformational heterogeneity generated from designed motions of antibody complexes or sampled from a molecular dynamics simulation, as well as compositional heterogeneity from mixtures of ribosome assembly states or 100 common complexes present in cells. We then analyze state-of-the-art heterogeneous reconstruction tools, including neural and non-neural methods, assess their sensitivity to noise, and propose new metrics for quantitative evaluation. We hope that CryoBench will be a foundational resource for accelerating algorithmic development and evaluation in the cryo-EM and machine learning communities. Project page: https://***. © 2024 Neural information processing systems foundation. All rights reserved.

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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4541-4546页

作者： Mishra, Pradeep Kumar Mishra, Gouri Sankar Abidi, Ali Imam Kumar, Rajiv Sahoo, Ashok Kumar Maini, Tarun Department of Computer Science and Application SSET Sharda University Department of Computer Science and Engineering SSET Sharda University NVIDIA AI Learning & amp Research Center Department of Applied Computational Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Graphic Era Hill University School of Computer Science and Engineering Galgotias University

It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as a person. There has been a lot of study on the possibility of deducing aspects of a person’s character from their handwriting. In this piece, we’ll look at the different personality qualities and how they might be utilized to make an evaluation. The report includes a dataset showing a correlation between several traits of personality. The Big Five Personality Traits are mapped using handwritten personality data. It was suggested that a comparison table should be used to spot distinctive character quirks. The article includes a review of previous studies and an assessment of the current status of the topic. Handwriting embodies have both creativity and expressiveness. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Agreeableness Conscientiousness Extraversion Neuroticism Openness

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