检索结果-内蒙古大学图书馆

IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Yunping Fang Chenglong Fu Xiaojiang Du Dept. of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Dept. of Software and Information Systems Univ. of North Carolina at Charlotte Charlotte NC USA

The Matter standard, formerly known as Connected Home over IP, has emerged as the preferred choice for most smart home IoT vendors and service providers for the next-generation smart home IoT systems. It enhances interoperability across different smart home ecosystems and introduces the multi-admin feature, allowing a device to be commissioned and managed by multiple platforms simultaneously. While this standard offers considerable convenience, it also presents challenges for security policy enforcement. Policy enforcement has been highlighted in various studies as a crucial countermeasure against smart home IoT system vulnerabilities. Existing smart home policy enforcement methods, designed for individual IoT admin platforms, operate under the assumption of having a global view and control over all IoT devices in a smart home. This assumption, however, is untenable in a multi-admin environment, where an admin may only have partial device access or a partial view. In this work, we thoroughly analyze these challenges in a multi-admin setting and propose the first cross-admin policy enforcement solution. Our solution can parse complex policies into deployable sub-policies for each admin, and create virtual device instances and virtual automation rules to interconnect various admins. We test our solution on a real-world testbed involving 12 IoT devices and three popular Matter-enabled IoT platforms. Our results show that our solution can enforce cross-admin policies with a 100% success rate and a very small delay.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Train a Robot to Climb Staircase Using Vision-Base System 10

Train a Robot to Climb Staircase Using Vision-Base System

引用

10th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2022

作者： Jayawardana, J.T.H. Dilshan, H.V.V. Wijethilaka, R.G.K.H Balasooriya, T.D. Rajapaksha, U.U.S. Harshanath, S.M.B. Jayawardena, Chandimal Sri Lanka Institute of Information Technology Dept. Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. Information Technology Malabe Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

Currently, robots are used for different types of work, such as the manufacturing industry, healthcare, and the hotel industry. According to the current epidemic situation, the usage of robots was increased because of the need to reduce human interaction. As a result, they have to walk around the workplace, because of that, they may have to climb staircases. The world has many types of robots. Here the selected robot is a humanoid. This proposal is concerned with how to detect the staircase, count steps, get dimensions of it, and move the robot on it by keeping body balance. First, want to know what the objects are, then walk. The identified images from the image sensors will get as input. The technology stack that is used for image analysis is a method related to computer vision in deep learning. Other than that, while climbing the stairs robot needs to identify whether the staircase is over or not. Here we introduce a new concept: get the number of steps required to climb before climbing the stairs. It is related to how humans identify things by seeing and making decisions. The need to take the dimensions of the stairs is that when considering the stairs, they have their height, width, and range. Therefore, it is imperative to calculate the dimensions of each staircase separately. Creating a balance system similar to human balance is a great advantage in robotics. To implement such a system, there is an upright pose controller to allow the robot to walk stably by preventing tilting of the robot during walking on uneven floor. In general, for us to do that we need to calculate the global inclination of the floor is a key factor. It can be measured with a 2-axis accelerator meter, and it is installed in the inertial sensor. © 2022 IEEE.

关键词： Stairs

来源：评论

学校读者我要写书评

暂无评论

Understanding Statistical Correlation of Application Security Vulnerability Data from Detection and Monitoring Tools 33

Understanding Statistical Correlation of Application Securit...

引用

33rd International Telecommunication Networks and Applications Conference, ITNAC 2023

作者： Kasturi, Santanam Li, Xiaolong Pickard, John Li, Peng Indiana State University Technology Management Terre Haute United States Indiana State University Dept. of Electronics and Computer Engineering Terre Haute United States East Carolina University Dept. of Technology Sys Greenville United States East Carolina University Dept. of Technology Systems Greenville United States

ISBN: (纸本)9798350317138

Vulnerability data gathered from multiple detection and monitoring capabilities at different layers of an application using a time-series analysis will provide value and insights by doing a statistical correlation with attack requests observed using a Web Application Firewall (WAF) monitoring solution. Static Analysis Security Testing, software Composition Analysis, Dynamic Analysis Security Testing (DAST), Application Ethical Hack, Application Programming Interface (API) testing are the tests / scans that have been used to gather vulnerability data for this study. Correlations can further help track abnormal transaction paths if we follow specific ones pointed out by the statistical analysis for those requests that are not blocked by the rules and are allowed as valid transactions to pass through. This provides a narrowed down focus on the convergence of observability and security, critical to realizing a near-real time rapid action. Observations must continue for many days as a time series ensuring consistency and reliability in data collections and analysis. Multiple applications must be observed in a similar manner for ensuring validity of the process for analysis. Also, gathering sufficient data that is large enough to represent a reasonable population of web applications within an organization is a significant factor in achieving reliable correlation. Applying Pearson Correlation (or Spearman Correlation for distributions that are not normal) technique provides insight into Significance (two-tailed) as to whether a correlation is present over large number of data points. Results of analysis show evidence of correlations among specific attack requests monitored by the WAF and corresponding vulnerabilities in applications, detected using one or more methods. This is significant to looking for more insights into how these correlations can further explored into predicting attack patterns based on existing vulnerabilities. © 2023 IEEE.

关键词： Time series analysis

来源：评论

学校读者我要写书评

暂无评论

Multi-Label Classification of CS Papers Using Natural Language Processing Models

Multi-Label Classification of CS Papers Using Natural Langua...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Zheng Zhang Amit Das Mostafa Rahgouy Yida Bao Sanjeev Baskiyar Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics Auburn University Auburn AL United States

computer science (CS) is a rapidly evolving field of knowledge with a significant volume and significance of scientific papers. Consequently, meticulous management and categorization of CS papers are crucial to ensure efficient access and retrieval of relevant information. This study aims to evaluate the effectiveness of eight traditional machine learning models, as well as Recurrent Neural Network (RNN) models, such as LSTM and BiLSTM, and state-of-the-art pre-trained self-attention-based natural language processing (NLP) models, including BERT, XLNet, RoBERTa, and DistilBERT, for the task of multi-label classification of arXiv CS paper classification. The results demonstrate that pre-trained self-attention-based models consistently outperform the other models regarding classification performance. Moreover, self-attention-based models exhibited superior performance and achieved a new state-of-the-art result. To the best of our knowledge, our paper represents the first endeavor to evaluate machine learning models, specifically on computer science-related documents, as part of a multi-label classification task.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Diabetes Prediction using Hybrid Feature Selection and Ensemble Learning with AdaBoost 8

Enhancing Diabetes Prediction using Hybrid Feature Selection...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Thuraka, Bharadwaj Pasupuleti, Vikram Kodete, Chandra Shikhi Chigurupati, Ravi Swaroop Tirumanadham, N. S. Koti Mani Kumar Shariff, Vahiduddin Northwest Missouri State University School of Computer Science and Information Systems MaryvilleMO64468 United States Eastern Illinois University School of Technology CharlestonIL61920 United States Dept. of Cse Bhimavaram India Sir C R Reddy College of Engineering Dept. of Cse Eluru India

ISBN: (纸本)9798350376425

This research relies on a feature selection method termed LasNO to analyse the progress of diabetes prediction. The ANOVA integrated with Lasso regularization identifies significant predictors like Age, BMI, DiabetesPedigreeFunction, and (Glucose). An AdaBoost model with the following characteristics was developed, and the results were exceptional performance metrics: 97.02% accuracy, 87.65% precision, and 82.5% recall. The results of the primary focus on glucose levels to differentiate between events with and without diabetes have been affirmed from the visual analyses. It also calls into attention that due to the appropriate cross-validation, LasNO is very successful on improving the interpretability and the possibility of generalization of the model. Such findings make it clear that efforts towards early detection of the condition and subsequent timely implementation of measures to contain the illness can go quite a long way in enhancing the quality of the lives of people that contract the disease. Further research is likely to be devoted to the assessing of the improved and broadened validation of the dataset as a critical approach for the enhancement and expansion of the model for use in clinics. As a result, this study aims to advance diabetes theragnostic possibilities and healthcare recommendations for better action based on ML approaches enhancing the accuracy of predictions. In this study, approaches to feature selection used for improving the performance while also making the models more understandable are the focus. The idea is to ensure that medical practitioners have reliable tools to undertake early detection and management of diabetes. Thus, the main objective of dealing with patients' Health-Related Quality of Life is to simultaneously increase the quality of patients' living while decreasing the costs of their treatment. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Cooperative Multi-Agent Assignment over Stochastic Graphs via Constrained Reinforcement Learning

arXiv

引用

arXiv 2025年

作者： Agorio, Leopoldo Van Alen, Sean Paternain, Santiago Calvo-Fullana, Miguel Bazerque, Juan Andres Dept. of Electrical and Computer Engineering Univ. of Pittsburgh PittsburghPA United States Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Dept of Engineering Universitat Pompeu Fabra Barcelona Spain

Constrained multi-agent reinforcement learning offers the framework to design scalable and almost surely feasible solutions for teams of agents operating in dynamic environments to carry out conflicting tasks. We address the challenges of multi-agent coordination through an unconventional formulation in which the dual variables are not driven to convergence but are free to cycle, enabling agents to adapt their policies dynamically based on real-time constraint satisfaction levels. The coordination relies on a light single-bit communication protocol over a network with stochastic connectivity. Using this gossiped information, agents update local estimates of the dual variables. Furthermore, we modify the local dual dynamics by introducing a contraction factor, which lets us use finite communication buffers and keep the estimation error bounded. Under this model, we provide theoretical guarantees of almost sure feasibility and corroborate them with numerical experiments in which a team of robots successfully patrols multiple regions, communicating under a time-varying ad-hoc network. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Mm-wave 3D-printed DRAs for Cost-effective Emerging 5G-and SatCom Applications

Mm-wave 3D-printed DRAs for Cost-effective Emerging 5G-and S...

引用

2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： El-Sawaf, Heba Abdel-Wahab, Wael M. Safavi-Naeini, Safieddin University of Waterloo Dept. of Electrical and Computer Engineering WaterlooON Canada C-COM Satellite Systems Inc. Ottawa Canada

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This paper presents a demonstration for low cost dielectric resonator antenna (DRA) manufacturing, using 3D-printing technology for mm-wave applications. DRAs are high-efficient radiators, as compared to metallic antennas, especially at high frequency applications. Moreover, 3D-printing technology facilitates DRA machining and positioning on the printed circuit board. Two DRAs, designed at 20 GHz, and 30 GHz, are 3D-printed, and measured realizing good performance. © 2022 IEEE.

关键词： 3D printers

来源：评论

学校读者我要写书评

暂无评论

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

引用

IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Woosung Kang Jinkyu Lee Youngmoon Lee Sangeun Oh Kilho Lee Hoon Sung Chwa Dept. of Electrical Engineering and Computer Science DGIST Republic of Korea Dept. of Computer Science and Engineering Sungkyunkwan University (SKKU) Republic of Korea Dept. of Robotics Hanyang University Republic of Korea Department of Software & Computer Engineering Ajou University Republic of Korea School of AI Convergence Soongsil University Republic of Korea

ISBN: (数字)9798350358414

ISBN: (纸本)9798350358421

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity.

关键词： Scheduling algorithms Memory management Graphics processing units Artificial neural networks Machine learning Drives Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Supervised Learning Approaches for Vibration Analysis and Anomaly Detection in Rotating Machines

Supervised Learning Approaches for Vibration Analysis and An...

引用

Emerging Trends in Electrical, Control, and Telecommunication engineering (ETECTE), International Conference on

作者： Syed Zulfiqar Ali Syed Hasnat Ali Anees Javeed Ihsan Ullah Sufyan Ali Memon Sajjad Manzoor Dept. of Electrical and Computer Engineering CUI Abbottabad Campus Abbottabad Pakistan Dept. of Defence Systems Engineering Sejong University Seoul Republic of Korea Dept. of Electrical Engineering MUST Mirpur AJK Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

Safety and reliability are crucial for modern systems and technologies. This paper presents an approach for real-time anomaly detection in rotating machines using vibration analysis and machine learning algorithms for predictive maintenance. Rotating machines such as motors, engines, gearboxes and turbines are widely used systems and faults in these systems can result in complete shutdowns. Preventing unexpected downtime while ensuring equipment reliability and safety is essential. In this context, anomaly detection plays a vital role in monitoring the healthy operation of rotating machines. A test-bed was designed to generate datasets for both balanced (healthy) and unbalanced (faulty) motor conditions. The experimental setup consists of a three-phase induction motor, a signal acquisition system (Raspberry Pi), a vibration sensor (MPU6050) and a coupling system capable of adjusting motor balance. Supervised machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF) were applied to classify and detect anomalies in the collected datasets. A grid search algorithm was employed for hyperparameter tuning of the SVM and RF models. The proposed technique achieved a maximum accuracy of approximately 98% using SVM and 100% using the RF algorithm leveraging nine optimally selected temporal features.

关键词： Support vector machines Vibrations Radio frequency Machine learning algorithms Approximation algorithms Vectors Safety Classification algorithms Reliability Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Downlink Covariance Estimation in URA FDD Massive MIMO systems

Downlink Covariance Estimation in URA FDD Massive MIMO Syste...

引用

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

作者： Salime Bameri Khalid Almahrog Ramy H. Gohary Amr El-Keyi Yahia Ahmed Dept. of Systems and Computer Engineering Carleton University Canada Ericsson Canada

We propose a low-complexity downlink channel covariance matrix estimation for massive multiple-input multiple-output systems in which the base station (BS) is equipped with a uniform rectangular antenna array (URA). This scheme can be expressed in the form of an affine transformation which depends only on the uplink and downlink carrier frequencies, and the BS array configurations. An upper bound on the estimation error is derived, which shows that the accuracy of the proposed scheme increases with the number of URA antennas and the compactness and differentiability class of the periodic extension of a non-linearly transformed version of the angular power spread. The performance superiority of the proposed scheme over its existing counterparts is confirmed through simulations.

关键词： Geometry Estimation error Upper bound Massive MIMO Signal processing Downlink Numerical simulation

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：