检索结果-内蒙古大学图书馆

30th IEEE International Conference on Telecommunications, ICT 2024

作者： Li, Wei Wu, Zhaonan Zhang, Bosheng Zheng, Li Zhao, Weibo Zou, Jiaqi Sun, Songlin School of Information and Communication Engineering Beijing University of Posts and Telecommunications China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology China

ISBN: (纸本)9798350356694

Remote Direct Memory Access (RDMA) has emerged as the predominant networking technology and plays an important role in the Artificial Intelligence Generated Content (AIGC) industry, which requires effective congestion control algorithms to unleash its performance. Since the data Center Quantized Congestion Notification (DCQCN) protocol was introduced in 2015, it has quickly become the standard congestion control solution across RDMA networks and an essential component of high-speed clusters and data center networks. However, as a congestion control (CC) algorithm that primarily relies on explicit congestion notification (ECN), DCQCN is challenged by delayed bandwidth convergence and sluggish congestion detection. To address these challenges, this paper proposes an optimized congestion control algorithm solution based on DCQCN. Specifically, we propose more accurate switch queue length monitoring, which is divided into two main parts: a congestion detection phase and a congestion control phase. Our algorithm utilizes the change rate of the switch queue length to refine and identify the degree of congestion and adjust the data sending rate after congestion occurs based on this change rate. Experimental results demonstrate that the proposed algorithm achieves better average flow completion time (FCT) in different scenarios including incast and actual network load, compared with DCQCN and other CC algorithms. © 2024 IEEE.

关键词： Queueing theory

来源：评论

学校读者我要写书评

暂无评论

ECLAIR: A Centralized Al-Powered Recommendations System in a Multi-Node EXAIT System 31

ECLAIR: A Centralized Al-Powered Recommendations System in a...

引用

31st International Conference on Computers in Education, ICCE 2023

作者： Wijerathne, Isanka Flanagan, Brendan Dai, Yiling Ogata, Hiroaki Academic Center for Computing Media Studies Kyoto University Japan Center for Innovative Research and Education in Data Science Kyoto University Japan

ISBN: (纸本)9786269689019

Educational recommender systems are increasingly becoming a core feature of modern educational systems. Often the recommender component of a system is tightly integrated, or might be remotely located without accessing data from other local systems This paper proposes a framework called ECLAIR in which local educational systems can work and share data with a global recommender system that spans multiple educational institutions. In particular an Al-driven recommendation system is intricately integrated within a multi-node learning management system. Situated at the intersection of large-scale data analysis and personalized education, ECLAIR efficiently processes heterogeneous data from diverse LMS, while ensuring data security and privacy. The proposed ECLAIR's architecture, data pipeline, and processing mechanisms are explored in detail, focusing on its seamless integration with the existing infrastructure. By leveraging MongoDB change streams and relational databases, ECLAIR guarantees real-time data synchronization, secure storage, and efficient processing. Its unique Ingestor tool transforms selected xAPI data points into a relational table format, bolstering system functionality. The successful launch of ECLAIR serves as a testament to the potential of Al in enhancing personalized learning experiences, improving data security, and bolstering system efficiency. Nevertheless the paper emphasizes the need for ongoing research, specifically concerning privacy-preserving mechanisms and efficient management of data heterogeneity. It demonstrates ECLAIRs pivotal role in the rapidly evolving landscape of eLearning and its potential for future advancements, scalability, and adaptability, setting a new precedent for Al-powered eLearning recommendation systems. © 2023 Asia-Pacific Society for Computers in Education.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Implementation of Topic Modeling in the Analysis of Topic Trends in SDGs Goal 6 research

Implementation of Topic Modeling in the Analysis of Topic Tr...

引用

2023 International Conference on Electrical Engineering and Informatics, ICEEI 2023

作者： Illia, Fathonah Nooraeni, Rani Suadaa, Lya Hulliyyatus Politeknik Statistika Stis Department of Statistical Computing Jakarta Indonesia Data Science Research Unit Politeknik Statistika Stis Jakarta Indonesia

ISBN: (纸本)9798350381290

SDGs Goal 6 is one of the crucial objectives within the SDGs framework, making fulfilling this goal highly significant as it determines the achievement of other SDGs. As a serious global issue, this goal can be addressed through scientific research and innovation. However, research on this goal is often not well-known, leading to a need for more connection between the academic world and practitioners in implementing available solutions. Consequently, identifying critical pathways through scientific research is essential to attain this goal effectively. One of the expedient approaches is the utilization of topic modeling, which also facilitates the analysis of research topic trends related to SDGs Goal 6. This study employs topic modeling on data sourced from SemanticScholar using the BERTopic method, which captures the comprehensive context of research articles. The research results indicate that studies focusing on SDGs Goal 6 are interconnected with other SDGs. The default BERTopic model, chosen as the optimal model, illustrates that there is a prevailing topic tendency in target 6.3 related to water quality. © 2023 IEEE.

关键词： Water quality

来源：评论

学校读者我要写书评

暂无评论

XAIMed-Net: Towards Explainable Brain Tumour Detection in 2D T1-weighted CE-MRI Images Using Transfer Learning 26

XAIMed-Net: Towards Explainable Brain Tumour Detection in 2D...

引用

26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Adegboro, Oluwabukola Ganepola, Vayangi Dietlmeier, Julia Mazo, Claudia O'Connor, Noel E. SFI Centre for Research Training in Machine Learning Dublin City University Ireland Insight SFI Research Centre for Data Analytics Dublin City University Ireland School of Computing Dublin City University Ireland

ISBN: (纸本)9781837242672

Malignant brain tumours comprise one of the deadliest types of cancer and their timely and accurate detection is receiving urgent attention. Radiology can benefit from the recent impressive progress of AI and deep learning that provide the non-invasive diagnostic opportunity. However, the challenge of explainable AI-based decision-making processes remains. In this work, we aim to take a step toward more explainable classification of brain tumours in medical images by designing a new convolutional architecture while also leveraging the advantages of transfer learning. Specifically, we introduce a new shallow convolutional head that can be added to a pre-trained ResNet50 model to produce EigenCAM-based importance heatmaps. We investigate pre-training based on the ImageNet and RadImageNet datasets. Furthermore, we also address the problem of weakly-supervised brain tumour segmentation in a challenging 2D T1-weighted CE-MRI dataset by developing an approach to post-process the resulting heatmaps. Our preliminary experimental results with the Dice Score Coefficient of 0.145 and the new objective distance metric μΔ = 59.55 show the potential of combining large pre-trained models with shallow convolutional architectures to localize tumours using only weak classification labels. Our code is publicly available at https://***/juliadietlmeier/Explainable_brain_tumor_detection_in_MRI. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid forecasting of demand flexibility: A top-down approach for thermostatically controlled loads

引用

Energy and AI 2025年 20卷

作者： Massidda, Luca Marrocu, Marino CRS4 Center for Advanced Studies Research and Development in Sardinia Italy National Research Center in High Performance Computing Big Data and Quantum Computing Italy

Demand-side flexibility is crucial to balancing supply and demand, as renewable energy sources are increasingly integrated into the energy mix, and heating and transport systems are becoming more and more electrified. Historically, this balancing has been managed from the supply side. However, the shift towards renewable energy sources limits the controllability of traditional fossil fuel plants, increasing the importance of demand response (DR) techniques to achieve the required flexibility. Aggregators participating in flexibility markets need to accurately forecast the adaptability they can offer, a task complicated by numerous influencing variables. Based on a top-down approach, this study addresses the problem of forecasting electricity demand in the presence of flexibility from thermostatically controlled loads. We propose a hybrid model that combines data-driven techniques for probabilistic estimation of electricity consumption with a disaggregation of electricity consumption to identify the fraction of thermal loads, subject to flexibility, which is simulated by a virtual battery model. The technique is applied to a synthetic dataset that simulates the response of a European neighborhood to demand response interventions. The results demonstrate the model's ability to accurately predict both the reduction in electricity demand during DR events and the subsequent rebound in consumption. The model achieves a mean absolute percentage error (MAPE) lower than 17.0%, comparable to the accuracy without flexibility. The results obtained are compared with a direct data-driven approach, demonstrating the validity and effectiveness of our model. © 2025

关键词： Demand response

来源：评论

学校读者我要写书评

暂无评论

Origin of the high lattice thermal conductivity of beryllium among the elemental metals

引用

Physical Review B 2024年第22期109卷 L220302-L220302页

作者： Yani Chen Guijian Pang Fanchen Meng Wu Li Institute for Advanced Study Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo 315200 China Research Computing and Data Clemson Computing and Information Technology

From first-principles calculations we reveal that beryllium has the highest lattice thermal conductivity (κph) among all elemental metals at room temperature. Specifically, the calculated κph is 104(125) Wm−1K−1, contributing ∼50% (60%) to the total thermal conductivity along the a(c) axis, contrary to the common belief that κph is negligible in metals. κph reach the maxima with values of ∼210 Wm−1K−1 for both axes at 125 K. The unusually high κph is related to the weak three-phonon scattering with a dip in the intermediate-frequency region, which arises from its high Debye temperature and bunched phonon dispersions. Another consequence of the weak three-phonon scattering is the strong effect of higher-order (fourth-order) anharmonicity and electron-phonon coupling on κph. We also predict that κph increases significantly with pressure, mainly due to the weakening of four-phonon scattering, and consequently exceeds the electronic contribution κe by more than one third in both axes at 20 GPa. Our work deepens the understanding of thermal transport in metals, and can benefit the search of metals with high thermal conductivity.

关键词： Anharmonic lattice dynamics Thermal conductivity Elemental metals Boltzmann theory

来源：评论

学校读者我要写书评

暂无评论

Winning with Losses: The Surprising Success of Negative Strategies in Social Interaction Behavior

引用

Physical Review Letters 2024年第16期133卷 167401页

作者： Joel Weijia Lai Kang Hao Cheong Institute for Pedagogical Innovation Research and Excellence School of Physical and Mathematical Sciences College of Computing and Data Science (CCDS)

In the vast network of social interactions and behaviors, it is common to find certain schemes that, on their own, might appear counterproductive to societal progress. When observed in isolation, these schemes often seem to hinder more than they help. However, due to society’s complexity, the hidden potential of combining these seemingly detrimental schemes often goes unnoticed. Here, we investigate two such social behaviors, reputation and reciprocity, and their role in explaining Darwin’s survival of the fittest, examining how these fundamental principles govern individual interactions and shape broader social dynamics. We outline the dynamics of these two social behaviors and underline the importance of combined strategies in enhancing group welfare and contributing to interdisciplinary research in social physics.

关键词： Animal behavior, social interactions & emergent phenomena Ecological networks Chaos & nonlinear dynamics

来源：评论

学校读者我要写书评

暂无评论

Runtime reconfiguration of data services for dealing with out-of-range stream fluctuation in cloud-edge environments

引用

Digital Communications and Networks 2022年第6期8卷 1014-1026页

作者： Shouli Zhang Chen Liu Xiaohong Li Yanbo Han Division of Intelligence and Computing Tianjin UniversityTianjin300072China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of TechnologyBeijing10009China Cloud Computing Research Center North China University of TechnologyBeijing10009China

The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data processing by moving services closer to the *** this connection,a key issue is to determine when and where services should be *** service deployment strategies used to be static based on the rules defined at the design ***,dynamically changing IoT environments bring about unexpected situations such as out-of-range stream fluctuation,where the static service deployment solutions are not *** this paper,we propose a dynamic service deployment mechanism based on the prediction of upcoming stream *** effectively predict upcoming workloads,we combine the online machine learning methods with an online optimization algorithm for service deployment.A simulation-based evaluation demonstrates that,compared with those state-of-the art approaches,the approach proposed in this paper has a lower latency of stream processing.

关键词： IoT stream processing Edge computing Out-of-Range stream fluctuation Dynamic service deployment

来源：评论

学校读者我要写书评

暂无评论

Exploring the Efficacy and Limitations of Histogram-Based Fake Image Detection 28th

Exploring the Efficacy and Limitations of Histogram-Based Fa...

引用

28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Hölscher, Dirk Reich, Christoph Gut, Frank Knahl, Martin Clarke, Nathan Institute for Data Science Cloud Computing and IT Security Furtwangen University Furtwangen78120 Germany Centre for Cyber Security Communications and Network Research Plymouth University Plymouth United Kingdom

Generative image models pose challenges to image authenticity and trustworthiness, blurring the line between real and fake content. This paper addresses these concerns by proposing a histogram-based approach using pre-trained models (vgg16, ResNet50, Xception) to train classification networks for distinguishing real from generated images. Leveraging histograms derived from images, the method aims to accurately classify images as authentic or synthetic. Through experiments, the paper examines the effectiveness of the approach in mitigating the risks associated with fake contents widespread dissemination. Results demonstrate promising advancements in detecting image manipulation and preserving the integrity of visual information amidst the spread of generative models. Using pre-trained models the paper shows high classification accuracy for detecting fake images. © 2024 The Authors.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Back-filling Missing data When Predicting Domestic Electricity Consumption From Smart Meter data 32

Back-filling Missing Data When Predicting Domestic Electrici...

引用

32nd Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2024

作者： Chen, Xianjuan Cai, Shuxiang Smeaton, Alan F. School of Computing Dublin City University GlasnevinDublin 9 Ireland Insight Research Ireland Centre for Data Analytics Dublin City University GlasnevinDublin 9 Ireland

This study uses data from domestic electricity smart meters to estimate annual electricity bills for a whole year. We develop a method for back-filling data smart meter for up to six missing months for users who have less than one year of smart meter data, ensuring reliable estimates of annual consumption. We identify five distinct electricity consumption user profiles for homes based on day, night, and peak usage patterns, highlighting the economic advantages of Time-of-Use (ToU) tariffs over fixed tariffs for most users, especially those with higher nighttime consumption. Ultimately, the results of this study empowers consumers to manage their energy use effectively and to make informed choices regarding electricity tariff plans. © 2024 Copyright for this paper by its authors.

关键词：

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：