检索结果-内蒙古大学图书馆

Novel load balancing mechanism for cloud networks using dilated and attention-based federated learning with Coati Optimization

引用

Scientific Reports 2025年第1期15卷 1-15页

作者： Kathole, Atul B. Singh, Viomesh Kumar Goyal, Ankur Kant, Shiv Savyanavar, Amit Sadanand Ubale, Swapnaja Amol Jain, Prince Islam, Mohammad Tariqul Department of Computer Engineering Dr. D. Y. Patil Institute of Technology Pimpri Pune 411018 India Artificial Intelligence and Data Science Vishwakarma Institute of Technology (VIT) Pune India Symbiosis Institute of Technology Symbiosis International (Deemed University) Maharashtra Pune 412115 India Department of Computer Science and Engineering (AI & DS) Greater Noida Institute of Technology (GNIOT) Delhi/NCR Greater Noida India Department of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University Maharashtra Pune 411038 India Information Technology Department Marathwada Mitra Mandal College of Engineering Pune India Department of Mechatronics Engineering Parul Institute of Technology Parul University Gujarat Vadodara 391760 India Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment UKM Bangi Selangor 43600 Malaysia

Load balancing (LB) is a critical aspect of Cloud Computing (CC), enabling efficient access to virtualized resources over the internet. It ensures optimal resource utilization and smooth system operation by distributing workloads across multiple servers, preventing any server from being overburdened or underutilized. This process enhances system reliability, resource efficiency, and overall performance. As cloud computing expands, effective resource management becomes increasingly important, particularly in distributed environments. This study proposes a novel approach to resource prediction for cloud network load balancing, incorporating federated learning within a blockchain framework for secure and distributed management. The model leverages Dilated and Attention-based 1-Dimensional Convolutional Neural Networks with bidirectional long short-term memory (DA-DBL) to predict resource needs based on factors such as processing time, reaction time, and resource availability. The integration of the Random Opposition Coati Optimization Algorithm (RO-COA) enables flexible and efficient load distribution in response to real-time network changes. The proposed method is evaluated on various metrics, including active servers, makespan, Quality of Service (QoS), resource utilization, and power consumption, outperforming existing approaches. The results demonstrate that the combination of federated learning and the RO-COA-based load balancing method offers a robust solution for enhancing cloud resource management. © The Author(s) 2025.

关键词： Cloud Dilated Federated learning Load balancing Random opposition of Coati Optimization algorithm Resource prediction Resource sharing

来源：评论

学校读者我要写书评

暂无评论

Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

引用

Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), IEEE Symposium on

作者： Matthew Andres Moreno Anika Ranjan Emily Dolson Luis Zaman Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and AI in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

Computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this “hereditary stratigraphy” approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation “surface”-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation “column”-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions.

关键词： Surface reconstruction Accuracy Annotations Heuristic algorithms Computational modeling Biological system modeling Stratigraphy Phylogeny History Bars

来源：评论

学校读者我要写书评

暂无评论

Ambiguity learning and data correlation in multi-cross modal cyber-physical systems for detecting fake information

引用

Cyber-Physical Systems 2025年

作者： Byeon, Haewon AlMahadin, Ghayth Bsoul, Qusay Quraishi, Aadam Soni, Mukesh Khan, Shahi Raza Shabaz, Mohammad Department of AI and Software Inje University Gimhae Korea Republic of Data Science Department College of Information Technology Mutah University Mutah Jordan Department of Cybersecurity College of Computer Sciences and Informatics Amman Arab University Amman Jordan M.D. Research Intervention Treatment Institute HoustonTX United States Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune Pune India MJMC Centre for Media and Mass Communication Jamia Hamdard Delhi India Model Institute of Engineering and Technology Jammu India

This paper proposes an improved cyber-physical false information detection model based on cross-modal ambiguous learning. This work introduced the IC2 LFD model with an emphasis on ‘text-image’ ambiguity, focusing on the main goal of intelligent multi-cross modal data fake information identification. The experiment revealed that the significance of text and images differs in intelligent multi-cross modal data fake information detection. The effectiveness of the model is verified on the twitter dataset, showing a 6% accuracy improvement compared to the baseline model and a 1.6% performance improvement over detection methods without dynamic weight allocation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Multi-Partner Project: Electric Vehicle data Acquisition and Valorisation: A Perspective from the OPEVA Project

Multi-Partner Project: Electric Vehicle Data Acquisition and...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Alper Kanak Salih Ergün İbrahim Arif Ali Serdar Atalay Serhat Ege İnanç Oguzhan Herkiloğlu Ahmet Yazıcı Yunus Sabri Kirca Muhammed Ozberk Alim Kerem Erdogmus Ali Kafalı Dilara Bayar Muhammed Oğuz Taş Luca Davoli Laura Belli Gianluigi Ferrari Badar Muneer Valentina Palazzi Luca Roselli Fabio Gelati Ergünler R&D Co.Ltd.(ERARGE) Isparta Turkiye Ergtech SP.Z.O.O. Warsaw Poland AI4SEC OÖ Tallinn Estonia Bitnet Bilişim Hizmetleri Ltd. Istanbul Turkiye Eskişehir Osmangazi University Eskişehir Turkiye ACD Data Engineering Eskişehir Turkiye INO Robotics Eskişehir Turkey Department of Engineering and Architecture University of Parma Parma Italy University of Perugia Perugia Italy Luna Geber Engineering s.r.l. Perugia Italy

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The OPtimization of Electric Vehicle Autonomy (OPEVA) project enhances data aggregation for Electric Vehicles (EVs) by collecting critical real-time data (i.e., vehicle performance, battery health, charging behaviours) through heterogeneous data acquisition devices built on robust HW and integrated with Internet of Things (IoT) protocols. By combining internal sensor data and driver-specific behaviours with external information (e.g., road conditions, charging station availability), OPEVA maximizes vehicles performance, establishing secure and seamless data communication between EVs and the infrastructure, and using IoT and cloud computing tools alongside Vehicle-to-Everything (V2X) devices and networks. This paper focuses on the extensible data model ensuring semantic data integrity considering in- and out-vehicle factors, presenting data acquisition solutions dealing with OPEVA's semantic data model and their use in various Artificial Intelligence (ai)-powered use cases (e.g., range prediction, route optimization, battery management).

关键词： Performance evaluation Cloud computing Roads data acquisition Semantics Electric vehicles data models Internet of Things Vehicle-to-everything Optimization

来源：评论

学校读者我要写书评

暂无评论

Evaluation of wizard-of-Oz and self-play data collection techniques for turkish goal-oriented dialogue agents

Evaluation of wizard-of-Oz and self-play data collection tec...

引用

2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021

作者： Arslan, Dogukan Eryigit, Gulsen Istanbul Technical University Department of AI and Data Engineering Istanbul Turkey

ISBN: (纸本)9781665436038

As with all natural language processing tasks, the lack of open-source training data required for the development of dialogue agents is a major obstacle to research studies in the field. Especially languages that are not widely studied, such as Turkish, suffer more from this problem. This article introduces a comparison of Wizard-of-Oz and self-play data collection techniques for Turkish goal-oriented dialogue system generation. Three data sets have been prepared and introduced to the researchers by using these techniques. Being the first publicly available human-to-human Turkish dialogue data sets, although open for development, the created resources from the restaurant domain are very valuable for further research on Turkish dialogue systems. The mentioned methods are quantitatively compared on the produced data sets, in terms of dialog act classification and slot identification scores. Since it is costly to collect data with methods like Wizard-Of-Oz in every domain, an open-source flexible and easy-to-use framework is also provided implementing self-play which may be used to create machine-to-machine dialogue outlines and speed data collection for low-resource languages like Turkish. Besides, designed templates of annotation screens for crowdsourcing are provided for future studies. © 2021 IEEE.

关键词： data acquisition

来源：评论

学校读者我要写书评

暂无评论

Spatial-Temporal Analysis of Collective Emotional Resonance in China During Global Health Crisis 34

Spatial-Temporal Analysis of Collective Emotional Resonance ...

引用

34th ACM Web Conference, WWW 2025

作者： Zhang, Limiao Qi, Xinyang Ma, Haiping Gao, Jie Zhang, Xingyi Hu, Yanqing Jin, Yaochu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Institutes of Physical Science and Information Technology Anhui University Hefei China School of Innovation and Entrepreneurship Shandong University Qingdao China School of Computer Science and Technology Anhui University Hefei China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen China Trustworthy and General AI Lab School of Engineering Westlake University Hangzhou China

ISBN: (纸本)9798400712746

The 21st century has already witnessed so many outbreaks with pandemic potential, including SARS (2002), H1N1 (2009), MERS (2012), Ebola (2014), Zika virus (2015), and the COVID-19 pandemic (2019). Using 60 million geotagged Sina Weibo tweets covering over 20 million active accounts, we investigate the collective emotional dynamics on social media in the most recent global pandemic, i.e., COVID-19. This research features two highlights: (1) It focuses on the Chinese population located in the initial epicenter of the pandemic. (2) It examines the initial year after the pandemic outbreak, a critical period where emotions were most intense due to the uncertainty and rapid developments related to the crisis. Using cross-disciplinary methods, we reveal a positive connection between online emotional resonance and geographic proximity, demonstrating a direct mapping between virtual network distances and physical spatial embedding. We propose a percolation-based index to measure the nationwide emotional resonance level with which we illustrate the significant economic impact of the global health issue. Finally, we identify a leader-follower pattern in emotional resonance fluctuations based on time-lag emotion correlations, revealing that less active regions play a crucial role in leading and responding to emotional changes. In the face of long COVID and emerging global health crises, our analysis elucidates how collective emotional resonance evolves, providing potential directions for online opinion interventions during global shocks. © 2025 Copyright held by the owner/author(s).

关键词： Emotional resonance Percolation theory Sentimental analysis Social network

来源：评论

学校读者我要写书评

暂无评论

LHQ-SVC: Lightweight and High Quality Singing Voice Conversion Modeling

LHQ-SVC: Lightweight and High Quality Singing Voice Conversi...

引用

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

作者： Yubo Huang Xin Lai Muyang Ye Anran Zhu Zixi Wang Jingzehua Xu Shuai Zhang Zhiyuan Zhou Weijie Niu School of Civil Engineering Southwest Jiaotong University China Zhenguan AI Lab ZhenGuan Innovation (Shenzhen) Technology Co. Ltd China SWJTU-Leeds Joint School Southwest Jiaotong University China School of Computing and Artificial Intelligence Southwest Jiaotong University China Shenzhen International Graduate School Tsinghua University China Department of Data Science New Jersey Institute of Technology USA School of Economics and Management Southwest Jiaotong University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Singing Voice Conversion (SVC) has emerged as a significant subfield of Voice Conversion (VC), enabling the transformation of one singer’s voice into another while preserving musical elements such as melody, rhythm, and timbre. Traditional SVC methods have limitations in terms of audio quality, data requirements, and computational complexity. In this paper, we propose LHQ-SVC, a lightweight, CPU-compatible model based on the SVC framework and diffusion model, designed to reduce model size and computational demand without sacrificing performance. We incorporate features to improve inference quality, and optimize for CPU execution by using performance tuning tools and parallel computing frameworks. Our experiments demonstrate that LHQ-SVC maintains competitive performance, with significant improvements in processing speed and efficiency across different devices. The results suggest that LHQ-SVC can meet real-time performance requirements, even in resource-constrained environments.

关键词： Performance evaluation Computational modeling Static VAr compensators Parallel processing Signal processing Rhythm Real-time systems Timbre Speech processing Tuning

来源：评论

学校读者我要写书评

暂无评论

Enhancing CNN-based network intrusion detection through hyperparameter optimization

Intelligent Systems with Applications

引用

Intelligent Systems with Applications 2025年 26卷

作者： Kaissar, Antanios Nassif, Ali Bou Soudan, Bassel Injadat, MohammadNoor College of Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science & AI Faculty of Information Technology Zarqa University Zarqa Jordan

This research investigates the optimization of hyperparameters in Convolutional Neural Networks (CNNs) to enhance the performance of Network Intrusion Detection Systems (NIDS). Four distinct optimization techniques, including Grid Search (GS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), are rigorously examined. Comprehensive experiments employ the UNSW-NB15 and CSE-CIC-IDS2018 datasets to determine the optimal hyperparameter configurations for each technique. Subsequently, detailed experiments evaluate the efficiency of the optimized models in detecting network attacks. The findings consistently reveal that optimized CNN models outperform their non-optimized counterparts across all optimization techniques. Notably, GWO emerges as the top-performing technique, achieving remarkable detection performance on both datasets, with 97.08 % accuracy, 96.95 % precision, 97.21 % recall, and 97.08 % F1 score on the UNSW-NB15 dataset, and 96.37 % accuracy, 96.13 % precision, 96.59 % recall, and 96.36 % F1 score on the CSE-CIC-IDS2018 dataset. Furthermore, hyperparameter optimization significantly reduces training and testing times. The GWO-optimized model achieved a reduction of >11 % in training time and 6.14 % in testing time on the UNSW-NB15 dataset. On the CSE-CIC-IDS2018 dataset, the GA-optimized model provided the best improvements, reducing training and testing times by 9.63 % and 8.61 %, respectively. In comparison to existing CNN models trained on the same datasets, the GWO-optimized CNN model consistently excels in all performance metrics, without the need for complex hybrid Deep Learning models. These results underscore the value of systematic hyperparameter optimization in enhancing CNN-based NIDS, with GWO standing out as a compelling technique for achieving optimal model configurations. © 2025 The Author(s)

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Scaling Neural-Network-Based Molecular Dynamics with Long-Range Electrostatic Interactions to 51 Nanoseconds per Day

arXiv

引用

arXiv 2025年

作者： Li, Jianxiong Zhang, Beining Li, Mingzhen Hu, Siyu Zeng, Jinzhe Liu, Lijun Yuan, Guojun Wang, Zhan Tan, Guangming Jia, Weile SKLP Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence and Data Science University of Science and Technology of China Suzhou Big Data & AI Research and Engineering Center Hefei China Department of Mechanical Engineering Graduate School of Engineering The University of Osaka Suita Japan

Neural network-based molecular dynamics (NNMD) simulations incorporating long-range electrostatic interactions have significantly extended the applicability to heterogeneous and ionic systems, enabling effective modeling critical physical phenomena such as protein folding and dipolar surface and maintaining ab initio accuracy. However, neural network inference and long-range force computation remain the major bottlenecks, severely limiting simulation speed. In this paper, we target DPLR, a state-of-the-art NNMD package that supports long-range electrostatics, and propose a set of comprehensive optimizations to enhance computational efficiency. We introduce (1) a hardware-offloaded FFT method to reduce the communication overhead;(2) an overlapping strategy that hides long-range force computations using a single core per node, and (3) a ring-based load balancing method that enables atom-level task evenly redistribution with minimal communication overhead. Experimental results on the Fugaku supercomputer show that our work achieves a 37x performance improvement, reaching a maximum simulation speed of 51 ns/*** Codes 82M37 Copyright © 2025, The Authors. All rights reserved.

关键词： Supercomputers

来源：评论

学校读者我要写书评

暂无评论

On-line parameter tuning for Monte-Carlo tree search in general game playing 30

On-line parameter tuning for Monte-Carlo tree search in gene...

引用

30th Benelux Conference on Artificial Intelligence, BNaiC 2018

作者： Sironi, Chiara F. Winands, Mark H.M. Games and AI Group Department of Data Science and Knowledge Engineering Maastricht University Netherlands

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：