检索结果-内蒙古大学图书馆

2023 IEEE International Conference on Big Data, BigData 2023

作者： Marek, Dariusz Paszkuta, Marcin Szygula, Jakub Biernacki, Piotr Domanski, Adam Szczygiel, Marta Krol, Marcel Wojciechowski, Konrad Silesian University of Technology Dept. of Distributed Systems and Informatic Devices Warsaw Poland Polish-Japanese Academy of Information Technology ul. Koszykowa 86 Warsaw02-008 Poland Silesian University of Technology Dept. of Computer Graphics Vision and Digital Systems Warsaw Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Warsaw Poland Silesian University of Technology Warsaw Poland

ISBN: (纸本)9798350324457

In the presented paper, detailed schematics and descriptions concerning communication in the context of swarm drone control are introduced. Methods and technologies behind them are discussed. The implementation of the presented concept was verified through a series of tests. Simulation results which confirm the effectiveness and efficiency of the proposed solutions are presented. The obtained results prove the correctness implementation of the presented methods and also demonstrate the benefits derived from the proposed approach. The presented concept of controlling a swarm of drones represents the current state of knowledge and technology in the field of drones and their control. By utilizing advanced communication technologies, positioning, and analysis of communication structures, this work makes a significant contribution to the UAV (Unmanned Aerial Vehicle) field. © 2023 IEEE.

关键词： Control Concepts Industrial Environment Industry 4.0 simulation environments Swarm of Drones UAV

来源：评论

学校读者我要写书评

暂无评论

Visual Gorgias: A Mechanism for the Visualization of an Argumentation Dialogue 2021

Visual Gorgias: A Mechanism for the Visualization of an Argu...

引用

25th Pan-Hellenic Conference on informatics

作者： Alexandros Vassiliades Ilias Papadimitriou Nick Bassiliades Theodore Patkos School of Informatics Aristotle University of Thessaloniki Greece and Institute of Computer Science Foundation for Research and Technology Greece School of Informatics Aristotle University of Thessaloniki Greece Institute of Computer Science Foundation for Research and Technology Greece

ISBN: (纸本)9781450395557

The area of eXplainable Artificial Intelligence (XAI) has been given great attention during the last decade, due to the obscurity that exists on how a data-driven model makes a decision. One idea in order to make Artificial Intelligence (AI) systems more explainable is to use cognitive methods that humans use when reasoning. For instance, argumentation is a common method that humans use when they explain an opinion [13]. In this paper, we introduce a framework that produces the visualization of an explanation from an argumentation dialogue, and can be easily understood by any user. More specifically, we utilize the argumentation tree that Gorgias returns [7] for the acceptability of an argument, to create a graph that represents the explanation of the argumentation tree.

关键词： Argumentation Dialogue Visualization Argumentation Explainability

来源：评论

学校读者我要写书评

暂无评论

Ultra-Short-Term Wind Power Forecasting Based on the Improved Dlinear Model

Ultra-Short-Term Wind Power Forecasting Based on the Improve...

引用

IEEE International Conference on Power and Energy Systems (ICPES)

作者： Peiyi Cai Cheng Peng Kun Zou Peijie Huang College of Mathematics and Informatics South China Agricultural University GuangZhou China School of Computer Science University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China

ISBN: (数字)9798350391329

ISBN: (纸本)9798350391336

Wind energy plays a more significant role in power grids due to its eco-friendly and renewable characteristics. This study proposes an improved prediction approach using the DLinear model, which incorporates the interactive learning module along with the channel and token attention mechanisms. The initial step involves decomposing the input time series into trend and seasonal components with a moving average kernel, followed by applying interactive learning to improve the modeling of these components. The combined framework demonstrates superior performance on multiple private datasets, significantly improving prediction accuracy and reliability compared to existing baseline models. The experimental findings show that the proposed method achieves a lower mean square error (MSE) and a lower mean absolute error (MAE) than several current models on two private datasets, thereby showcasing the combined effectiveness of these two modules.

关键词： Renewable energy sources Attention mechanisms Wind energy Time series analysis Mean square error methods Predictive models Wind power generation Power grids Reliability Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

Decomposition of Differences of Independent and Identically Distributed Random Variables

引用

Moscow University Computational Mathematics and Cybernetics 2025年第1期49卷 67-72页

作者： Ushakov, V. G. Ushakov, N. G. Department of Computational Mathematics and Cybernetics Moscow State University Moscow Russia Institute of Informatics Problems Federal Research Center ‘‘Computer Science and Control’’ Russian Academy of Sciences Moscow Russia Norwegian University of Science and Technology Trondheim Norway

The authors consider conditions where the distribution of the components of the difference between two independent identically distributed random variables can be uniquely reconstructed with an accuracy of up to a shift and reflection. This uniqueness is essential for solving a number of characterization problems in mathematical statistics. An algorithm for estimating the components is presented for when data are given in a symmetrized form.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dynamic Disaster Management with Real-Time IoT Data Analysis and Response

Dynamic Disaster Management with Real-Time IoT Data Analysis...

引用

2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Dankan Gowda, V. Sharma, Avinash Prasad, K.D.V. Saxena, Rini Barua, Tarkeshwar Mohiuddin, Khalid Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India Symbiosis Institute of Business Management Hyderabad India Pune India King Khalid University College of Business Department of Business Informatics PO Box-3247 Abha61471 Saudi Arabia

ISBN: (纸本)9798350382723

As natural and manmade disasters grew in number, as a result the problem of how to quickly and effectively respond to disaster has become fresh. This is precisely the purpose of this research: Using IoT technologies and powerful data analysis techniques, to integrate them into existing disaster management systems. The beginning of the article contains a broadcasted statement to the effect that traditional methods are insufficient, and real-time data and preemptive response systems are needed. With the application of Internet of Things, an integrated system is proposed in which countless types of information such as weather conditions and seismic activity are gathered by sensors and actuators. Advanced machine learning algorithms and predictive modeling are used to analyze the gathered data. This allows us to make real-time decisions. The design and construction of an IoT-based disaster management system is the methodology behind the research. In particular, we will evaluate how effective it is at reducing response times and increasing overall resilience to disasters. The results show a high efficiency in response, which reflects the feasibility of the method. Finally, the paper discusses the problems encountered in implementing IoT and advanced data analysis of disaster risk and suggests future research avenues. There is no doubt that they will change the present disaster management practice forever. © 2024 IEEE.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Data Cleaning Process for mHealth Log Data to Inform Health Worker Performance

Data Cleaning Process for mHealth Log Data to Inform Health ...

引用

作者： Savai, Simon Muhindi Hasan, Md Kamrul Kamano, Jemimah Misoi, Lawrence Wakholi, Peter Were, Martin C. Institute of Biomedical Informatics Moi University Eldoret Kenya Department of Computer Science Vanderbilt University TN United States School of Medicine Moi University Eldoret Kenya Moi Teaching and Referral Hospital Eldoret Kenya School of Computing and Informatics Technology Makerere University Kampala Uganda Department of Biomedical Informatics VUMC Nashville TN United States

ISBN: (纸本)9781643682907

Log data, captured during use of mobile health (mHealth) applications by health providers, can play an important role in informing nature of user engagement with the application. The log data can also be employed in understanding health provider work patterns and performance. However, given that these logs are raw data, they require robust cleaning and curation if accurate conclusions are to be derived from analyzing them. This paper describes a systematic data cleaning process for mHealth-derived logs based on Broeck's framework, which involves iterative screening, diagnosis, and treatment of the log data. For this study, log data from the demonstrative mUzima mHealth application are used. The employed data cleaning process uncovered data inconsistencies, duplicate logs, missing data within logs that required imputation, among other issues. After the data cleaning process, only 39,229 log records out of the initial 91,432 usage logs (42.9%) could be included in the final dataset suitable for analyses of health provider work patterns. This work highlights the significance of having a systematic data cleaning approach for log data to derive useful information on health provider work patterns and performance. © 2022 The authors and IOS Press.

关键词： Developing countries

来源：评论

学校读者我要写书评

暂无评论

Exponentially Fast Collaborative Broadcasting in 3D Space

Exponentially Fast Collaborative Broadcasting in 3D Space

引用

Information and Network Technologies (ICINT), International Conference on

作者： Yotam Ashkenazi Shlomi Dolev Sergey Frenkel Manish Kumar Sweta Kumari Ram Prasadh Narayanan Archit Somani Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel The Institute of Informatics Problems Russian Academy of Science Russia Department of Computer Science New Jersey Institute of Technology New Jersey United States Department of Computer Science and Engineering Shiv Nadar University Delhi India Institute for Information and Communication Systems Science Walton Institute Ireland

ISBN: (数字)9798331536756

ISBN: (纸本)9798331536763

Broadcasting stands as a vital function within wireless sensor networks (WSNs), where nodes disseminate messages throughout the entire network, radiating in all possible directions. Our focus lies on the issue of three-dimensional broadcasting, involving n sensors/robots positioned uniformly across a three-dimensional space. We propose an exponentially fast hybrid collaborative broadcasting algorithm in three-dimensional space "growing ball" through successive propagation steps. We study the effect of various parameters such as the density of the growing ball (ρ), distance (d), the energy emitted by node (E emit ), and ϵ used by the collaborative broadcast. Our approach is based on the idea of dividing the 3D space into two halves that reduce mathematical intricacy. The number of nodes N k in the k th step of the growing ball is the factor of (1+ϵ) k implies the exponential fast broadcast.

关键词： Wireless sensor networks Three-dimensional displays Collaboration Broadcasting

来源：评论

学校读者我要写书评

暂无评论

Evading IoT Intrusion Detection Systems with GAN

Evading IoT Intrusion Detection Systems with GAN

引用

Asia Joint Conference on Information Security (Asia JCIS)

作者： Mariama Mbow Rodrigo Roman Takeshi Takahashi Kouichi Sakurai Department of Informatics Kyushu University Japan Network Information and Computer Security (NICS) Lab University of Malaga Spain National Institute of Information and Communications Technology Tokyo Japan

ISBN: (数字)9798350380149

ISBN: (纸本)9798350380156

Network intrusion detection systems are essential tools used in cybersecurity to detect malicious traffic. They are used in the Internet of Things (IoT) to monitor the network traffic and identify infected IoT devices. Recently, machine learning and deep learning models have been increasingly adopted to enhance detection systems. However, these model-based detectors are vulnerable to adversarial attacks. This paper investigates the vulnerability of these machine learning-based NIDS models in the IoT environment. We propose a practical targeted black-box attack based on Generative Adversarial Networks to generate adversarial DDoS and Mirai traffic that can evade the detector. Experimental results show that the proposed approach deceived the detector to misclassify the attack traffic as benign and achieved a 100% evasion success rate. Also, the proposed approach can be transferred to other models, achieving different levels of effectiveness.

关键词： Deep learning Network intrusion detection Closed box Detectors Telecommunication traffic Generative adversarial networks Internet of Things Object recognition computer crime Monitoring

来源：评论

学校读者我要写书评

暂无评论

ReSup: Reliable Label Noise Suppression for Facial Expression Recognition

引用

IEEE Transactions on Affective Computing 2025年

作者： Zhang, Xiang Lu, Yan Yan, Huan Huang, Jinyang Gu, Yu Ji, Yusheng Liu, Zhi Liu, Bin University of Science and Technology of China School of Cyber Science and Technology Hefei230026 China Hefei University of Technology School of Computer and Information Hefei230601 China AI Lab Shanghai200030 China Guizhou Normal University School of Computer and Information Guiyang230601 China National Institute of Informatics Tokyo101-8430 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan

Because of the ambiguous and subjective property of the facial expression, the label noise is widely existing in the FER dataset. For this problem, in the training phase, current methods often directly predict whether the label is noised or not, aiming to reduce the contribution of the noised data. However, we argue that this kind of method suffers from the low reliability of such noise data decision operation. It makes that some mistakenly abounded clean data are not utilized sufficiently and some mistakenly kept noised data disturbing the model learning. In this paper, we propose a more reliable noise-label suppression method called ReSup. First, instead of directly predicting noised or not, ReSup makes the noise data decision by modeling the distribution of noise and clean labels simultaneously according to the disagreement between the prediction and the target. Specifically, to achieve optimal distribution modeling, ReSup models the similarity distribution of all samples. To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression. Specifically, ReSup utilize the property that two networks are less likely to make the same mistakes, making two networks swap decisions and tending to trust decisions with high agreement. Extensive experiments on popular datasets shows the effectiveness of ReSup. © 2010-2012 IEEE.

关键词： Affective computing facial expression label noise label noise modeling label noise suppression

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Performance of Semi-supervised Electricity Theft Detection in Smart Grids with Feature Engineering and Ensemble Learning

Enhancing the Performance of Semi-supervised Electricity The...

引用

IEEE Kansas Power and Energy Conference

作者： Ruobin Qi Wynter Japp Stephen Pan Jun Zheng Sihua Shao Department of Computer Science and Engineering New Mexico Institute of Mining and Technology Socorro NM School of Computing and Informatics University of Louisiana Lafayett Lafayette LA Department of Computer Science University of Arizona Tucson AZ Department of Electrical Engineering New Mexico Institute of Mining and Technology Socorro NM

ISBN: (数字)9798350372403

ISBN: (纸本)9798350372410

Electricity theft is a type of cyberattack posing significant risks to the security of smart grids. Semi-supervised outlier detection (SSOD) algorithms utilize normal power usage data to build detection models, enabling them to detect unknown electricity theft attacks. In this paper, we applied feature engineering and ensemble learning to improve the detection performance of SSOD algorithms. Specifically, we extracted 22 time-series and wavelet features from load profiles, which served as inputs for the seven popular SSOD algorithms investigated in this study. Experimental results demonstrate that the proposed feature engineering greatly enhances the performance of SSOD algorithms to detect various false data injection (FDI) attacks. Furthermore, we constructed bagged ensemble models using the best-performing SSOD algorithm as the base model, with results indicating further improvements in detection performance compared to the base model alone.

关键词： Wavelet domain Electricity Feature extraction Data models Smart meters Smart grids Ensemble learning

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：