检索结果-内蒙古大学图书馆

35th Benelux Conference on Artificial Intelligence and Machine Learning, BNAIC/Benelearn 2023

作者： Lenz, Oliver Urs Peralta, Daniel Cornelis, Chris Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Research Group for Computational Web Intelligence Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Information Technology Ghent University Ghent Belgium

ISBN: (纸本)9783031746499

By filling in missing values in datasets, imputation allows these datasets to be used with algorithms that cannot handle missing values by themselves. However, missing values may in principle contribute useful information that is lost through imputation. The missing-indicator approach can be used in combination with imputation to instead represent this information as a part of the dataset. There are several theoretical considerations why missing-indicators may or may not be beneficial, but there has not been any large-scale practical experiment on real-life datasets to test this question for machine learning predictions. We perform this experiment for three imputation strategies and a range of different classification algorithms, on the basis of twenty real-life datasets. In a follow-up experiment, we determine attribute-specific missingness thresholds for each classifier above which missing-indicators are more likely than not to increase classification performance. And in a second follow-up experiment, we evaluate numerical imputation of one-hot encoded categorical attributes. We reach the following conclusions. Firstly, missing-indicators generally increase classification performance. Secondly, with missing-indicators, nearest neighbour and iterative imputation do not lead to better performance than simple mean/mode imputation. Thirdly, for decision trees, pruning is necessary to prevent overfitting. Fourthly, the thresholds above which missing-indicators are more likely than not to improve performance are lower for categorical attributes than for numerical attributes. Lastly, mean imputation of numerical attributes preserves some of the information from missing values. Consequently, when not using missing-indicators it can be advantageous to apply mean imputation to one-hot encoded categorical attributes instead of mode imputation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Transformer-based Multivariate Time Series Anomaly Localization

arXiv

引用

arXiv 2025年

作者： Shimillas, Charalampos Malialis, Kleanthis Fokianos, Konstantinos Polycarpou, Marios M. KIOS Research Innovation Center of Excellence Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Mathematics and Statistics University of Cyprus Nicosia Cyprus

With the growing complexity of Cyber-Physical Systems (CPS) and the integration of Internet of Things (IoT), the use of sensors for online monitoring generates large volume of multivariate time series (MTS) data. Consequently, the need for robust anomaly diagnosis in MTS is paramount to maintaining system reliability and safety. While significant advancements have been made in anomaly detection, localization remains a largely underexplored area, though crucial for intelligent decision-making. This paper introduces a novel transformer-based model for unsupervised anomaly diagnosis in MTS, with a focus on improving localization performance, through an in-depth analysis of the self-attention mechanism's learning behavior under both normal and anomalous conditions. We formulate the anomaly localization problem as a three-stage process: time-step, window, and segment-based. This leads to the development of the Space-Time Anomaly Score (STAS), a new metric inspired by the connection between transformer latent representations and space-time statistical models. STAS is designed to capture individual anomaly behaviors and inter-series dependencies, delivering enhanced localization performance. Additionally, the Statistical Feature Anomaly Score (SFAS) complements STAS by analyzing statistical features around anomalies, with their combination helping to reduce false alarms. Experiments on real world and synthetic datasets illustrate the model's superiority over state-of-the-art methods in both detection and localization tasks. © 2025, CC BY-SA.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Hybrid Profile based Multi-document Text Summarisation

引用

Procedia computer Science 2025年 252卷 862-872页

作者： Jean Louis K.E. Fendji Dounia Donatien Marcellin Atemkeng Department of Computer Engineering University Institute of Technology University of Ngaoundere 455 Ngaoundere Cameroon Stellenbosch Institute for Advanced Study (STIAS) Wallenberg Research Centre at Stellenbosch University Stellenbosch South Africa Department Mathematics and Computer Science Faculty of Science University of Ngaoundere 454 Ngaoundere Cameroon Department of Mathematics Rhodes University P.O. Box 6140 Grahamstown South Africa

The internet has become a crucial component of the daily routines, offering numerous resources and documents for a variety of tasks and information retrieval. However, the large volume of available information often leads to ”information saturation,” posing a challenge to efficient processing and extraction of relevant information. To mitigate this issue, extensive research has been conducted exploring a range of methods, including machine learning and deep learning techniques. A significant advancement in this field is automatic text summarisation, which employs Natural Language Processing (NLP). Despite their efficacy, traditional summarisation methods typically fall short as they fail to consider the unique needs and preferences of individual users. This study introduces a novel, hybrid and profile-based multi-document summarisation method that selects relevant documents according to user queries and preferences, as defined in a user profile. By leveraging NLP algorithms, the proposed system creates personalised summaries by initially extracting sentences from documents that closely match the user’s profile, followed by the generation of a concise abstract summary. The model, specifically developed for French, results in a success rate of 87.5%, and delivering semantically coherent summaries for up to three documents concurrently. This method enhances the user experience by providing succinct and customised information.

关键词： Text Summarisation User Profile Multi-document preferences

来源：评论

学校读者我要写书评

暂无评论

Teaching An Old Dog New Tricks: Porting Legacy Code to Heterogeneous Compute Architectures With Automated Code Translation

arXiv

引用

arXiv 2025年

作者： Nytko, Nicolas Reisner, Andrew Moulton, J. David Olson, Luke N. West, Matthew Department of Computer Science University of Illinois at Urbana-Champaign United States Applied Computer Science Los Alamos National Laboratory New Zealand Applied Mathematics and Plasma Physics Los Alamos National Laboratory New Zealand Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign United States

Legacy codes are in ubiquitous use in scientific simulations;they are well-tested and there is significant time investment in their use. However, one challenge is the adoption of new, sometimes incompatible computing paradigms, such as GPU hardware. In this paper, we explore using automated code translation to enable execution of legacy multigrid solver code on GPUs without significant time investment and while avoiding intrusive changes to the codebase. We developed a thin, reusable translation layer that parses Fortran 2003 at compile time, interfacing with the existing library Loopy (Klöckner 2014) to transpile to C++/GPU code, which is then managed by a custom MPI runtime system that we created. With this low-effort approach, we are able to achieve a payoff of an approximately 2–3× speedup over a full CPU socket, and 6× in multi-node settings. © 2025, CC BY-SA.

关键词： Investments

来源：评论

学校读者我要写书评

暂无评论

Characteristics of nanoepoxy composite through structural, thermal and machine learning-enhanced dielectric analysis

引用

Journal of Polymer research 2025年第5期32卷 1-13页

作者： Thakor, Sanketsinh Jain, Prince Joshi, Anand Vaja, Chandan R. Parikh, Swapnil Panchal, Piyush Department of Applied Science and Humanities Parul Institute of Technology Parul University Gujarat Vadodara391760 India Department of Mechatronics Engineering Parul Institute of Technology Parul University Gujarat Vadodara391760 India Department of Physics School of Sciences Gujarat University Gujarat Ahmedabad380009 India Department of Computer Science Engineering Parul Institute of Technology Parul University Gujarat Vadodara391760 India Department of Physics Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India Micro Nano Research and Development Center Parul University Gujarat Vadodara391760 India

This study aims to comprehensively explore the characteristics of hybrid nano fillers incorporated epoxy composites (NFIEC) using various analytical techniques. The research encompasses machine learning-enhanced dielectric analysis across a wide frequency spectrum ranging from 100 Hz to 2 MHz. The structural properties of the nano-fillers, pristine epoxy (PE), and NFIEC are investigated through X-ray Diffraction (XRD). Hybrid nano fillers, including combinations like SiO2 + Fe3O4, ZnO + Fe3O4, TiO2 + Fe3O4 and Al2O3 + Fe3O4, are added to the PE at a concentration of 1 wt.% to form NFIEC. Additionally, Differential Scanning Calorimetry are employed to gain further insights into the thermal behaviour of the NFIEC. This study also demonstrates the effectiveness of machine learning models, particularly Extra Trees, CatBoost, XGBoost, and Ensemble Averaging, in predicting dielectric properties (Ε′ and Ε′′) of NFIEC using data from LCR and VNA measurements. The models exhibited high predictive accuracy, with Ensemble Averaging consistently delivering robust performance across datasets, highlighting the potential of ML techniques in advancing material science by reducing experimental efforts and enhancing predictive precision. © The Polymer Society, Taipei 2025.

关键词： Hybrid composites

来源：评论

学校读者我要写书评

暂无评论

Transformer-Based Multivariate Time Series Anomaly Localization

Transformer-Based Multivariate Time Series Anomaly Localizat...

引用

Computational Intelligence on engineering/Cyber Physical Systems (CIES), IEEE Symposium on

作者： Charalampos Shimillas Kleanthis Malialis Konstantinos Fokianos Marios M. Polycarpou KIOS Research and Innovation Center of Excellence Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Mathematics and Statistics University of Cyprus Nicosia Cyprus

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

With the growing complexity of Cyber-Physical Systems (CPS) and the integration of Internet of Things (IoT), the use of sensors for online monitoring generates large volume of multivariate time series (MTS) data. Consequently, the need for robust anomaly diagnosis in MTS is paramount to maintaining system reliability and safety. While significant advancements have been made in anomaly detection, localization remains a largely underexplored area, though crucial for intelligent decisionmaking. This paper introduces a novel transformer-based model for unsupervised anomaly diagnosis in MTS, with a focus on improving localization performance, through an in-depth analysis of the self-attention mechanism's learning behavior under both normal and anomalous conditions. We formulate the anomaly localization problem as a three-stage process: time-step, window, and segment-based. This leads to the development of the SpaceTime Anomaly Score (STAS), a new metric inspired by the connection between transformer latent representations and spacetime statistical models. STAS is designed to capture individual anomaly behaviors and inter-series dependencies, delivering enhanced localization performance. Additionally, the Statistical Feature Anomaly Score (SFAS) complements STAS by analyzing statistical features around anomalies, with their combination helping to reduce false alarms. Experiments on real world and synthetic datasets illustrate the model's superiority over state-of-the-art methods in both detection and localization tasks.

关键词： Location awareness Measurement Time series analysis Transformers Sensor systems Safety Internet of Things Reliability Monitoring Synthetic data

来源：评论

学校读者我要写书评

暂无评论

Blockchain Empowered Secure Federated Learning for Consumer IoT Applications in Cloud-Edge Collaborative Environment

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Kumar, Mohit Samriya, Jitendra Kumar Walia, Guneet Kaur Verma, Prabal Wu, Huaming Gill, Sukhpal Singh National Institute of Technology Department of Information Technology Jalandhar India Indian Institute of Information Technology Computer Science and Engineering Sonepat India National Institute of Technology Department of Information Technology Srinagar India Tianjin University Center for Applied Mathematics Tianjin China Queen Mary University of London School of Electronic Engineering and Computer Science United Kingdom

The growing number of consumer Internet of Things (IoT) gadgets, including smart homes, fitness trackers, connected appliances, and home security systems, is transforming the way we live our daily lives. This has led to the emergence of a collaborative cloud-edge paradigm to leverage resources and services near the end-user, thereby providing prompt response to delay-sensitive real-time applications. Nevertheless, the tremendous amount of data generated by various IoT devices and sent over the network is always an open security challenge. The introduction of Federated Learning (FL) addresses the security and data privacy shortcomings of traditional centralised machine learning. Despite FL's use for data privacy, it must overcome a number of significant challenges, such as privacy concerns, communication overhead, stragglers, and heterogeneity. To solve these challenges, this paper proposes a novel technique for enhancing security in IoT-enabled edge cloud computing networks, utilising blockchain-driven FL and Gaussian Bayesian transfer convolutional neural network architectures for data analysis. Blockchain-driven FL ensures the security and privacy of consumer IoT applications. In comparison to state-of-the-art works, the experimental results achieved throughput of up to 89%, latency of 71%, training accuracy of 91%, validation accuracy of 96%, and network security of 92%. © 1975-2011 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Radiative heat transfer in MHD copper-based polymer nanofluid over a sphere using larger radius and inter particle spacing of nanoparticles

Results in Engineering

引用

Results in engineering 2025年 26卷

作者： Darvesh, Adil Boukhris, Imed Collantes Santisteban, Luis Jaime Al-Buriahi, M.S. Elqahtani, Zainab Mufarreh Akgül, Ali García, Lucerito Katherine Ortiz Hassani, Murad Khan Department of Mathematics and Statistics Hazara University Mansehra21300 Pakistan Department of Physics Faculty of Science King Khalid University P.O. Box 960 Abha Saudi Arabia Department of Mathematics Universidad Nacional Pedro Ruiz Gallo Lambayeque Peru Department of Physics Sakarya University Sakarya Turkey Department of Physics College of science Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Topkapı Istanbul34010 Turkey Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia Mersin 10 Turkey Applied Science Research Center. Applied Science Private University Amman Jordan Universidad Nacional Pedro Ruiz Gallo Lambayeque Peru Ghazni University Department of Mathematics Afghanistan

The impact of nanoparticle size and interparticles spacing play a crucial role in fluid theology due to its significance on enhance heat transfer performance, which is crucial in many engineering and industrial processes particularly in thermal management systems. Due to this attention, the proposed study aims to explore the influence of nanoparticle radius and inter-particle spacing on thermal transport in a Copper (Cu) Polymer nanofluid over a sphere with radiative and magnetohydrodynamic (MHD) effects. Physical model incorporates Carreau fluid viscosity model. In addition, with incorporation of exponential heat generation and thermal radiation, the analysis reveals how tuning nanoparticle geometry significantly enhances heat transfer performance, which is critical for advanced thermal management systems. The governing nonlinear partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs) using similarity transformations and solved using the well-known bvp4c computational method. Results of proposed study indicate that larger nanoparticles and wider inter-particle spacing improve thermophysical and rheological behavior of fluid specifically thermal conductivity and fluid viscosity, which enhanced heat transport and intensified the temperature profile. Conversely, velocity profile decreases for smaller particle sizes and spacing but improves with increased nanoparticle dimensions. © 2025 The Author(s)

关键词： Radiant heating

来源：评论

学校读者我要写书评

暂无评论

MWRS: A MAB-based Worker Recruitment Scheme with Tripartite Stackelberg Game for Reliable Mobile Crowdsensing

引用

IEEE Transactions on Mobile Computing 2025年

作者： Ouyang, Yan Zeng, Feng Xiong, Neal N. Liu, Anfeng Pedrycz, Witold School of Computer Science and Engineering Central South University Changsha410083 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istinye University Istanbul Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Mobile Crowdsensing (MCS) has emerged as a compelling paradigm for data sensing and collection, leveraging the widespread adoption of mobile devices and the active participation of numerous users. Despite its potential, MCS faces critical challenges, particularly in recruiting reliable workers and acquiring high-quality sensing data. Most existing approaches assume prior information on worker quality and are vulnerable to collusion attacks, especially having not comprehensively considered workers' reliability and stability. To address these problems, we propose a Multi-Armed Bandit (MAB) based Worker Recruitment Scheme (MWRS) integrated with the Tripartite Stackelberg Game (TSG) for MCS. Specifically, a trust evaluation and truth inference mechanism is introduced to assess the trustworthiness of workers through active truth detection. To enhance recruitment quality, we employ a trust-aware worker selection mechanism that utilizes a modified Upper Confidence Bound (UCB) algorithm, achieving an optimal balance between exploration and exploitation. Furthermore, the interactions among participants are modeled using a TSG framework, which formulates their respective payoffs to determine optimal decision-making strategies, thus achieving mutually beneficial outcomes. Extensive evaluations on real-world datasets demonstrate that our proposed scheme improves total quality by up to 30.8% and reduces regret by up to 80.3% compared to existing methods. © 2025 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Phototropic growth algorithm: A novel metaheuristic inspired from phototropic growth of plants

引用

Knowledge-Based Systems 2025年 322卷

作者： Bohat, Vijay Kumar Hashim, Fatma A. Batra, Harshit Abd Elaziz, Mohamed Department of Computer Science & Engineering Netaji Subhas University of Technology Dwarka New Delhi110078 India Faculty of Engineering Helwan University Helwan Egypt Applied Science Research Center. Applied Science Private University Amman Jordan Centre of Excellence in AI Netaji Subhas University of Technology Dwarka New Delhi110078 India Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt College of Engineering and Information Technology Ajman University Ajman United Arab Emirates

This paper presents a novel optimization algorithm called the Phototropic Growth Algorithm (PGA), which takes its inspiration from the growth patterns of plant cells in response to sunlight. The proposed algorithm has been evaluated on two benchmark test function suites, the CEC 2017 and CEC 2020, and compared to other established metaheuristic-optimization algorithms based on the quality of the solutions obtained. The effectiveness of PGA is further demonstrated by testing it on six challenging engineering design problems with constraints, showcasing its robustness in solving real-world problems with unknown search spaces. Simulation results show that PGA outperforms other competitive algorithms by consistently generating superior solutions and demonstrates strong potential for solving complex optimization tasks using a nature-inspired approach. © 2025

关键词： Particle swarm optimization (PSO)

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：