检索结果-内蒙古大学图书馆

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Babanejaddehaki, Ghazaleh An, Aijun Davoudi, Heidar York University Department of Electrical Engineering and Computer Science Toronto Canada Ontario Tech University Faculty of Science Toronto Canada

ISBN: (纸本)9798350337488

Rapidly spreading diseases pose a significant threat, leading to substantial loss of life and economic devastation, as seen in the global COVID-19 outbreaks. Developing disease prediction models is crucial for preemptive pandemic control and minimizing their impact. As internet accessibility grows through computers and mobile devices, social media platforms provide a direct conduit to disseminate vital health information to the public. Unlike traditional methods that rely on bureaucratic channels, these platforms offer accurate and timely information distribution. We propose a framework that employs ontology to identify these symptoms and gather relevant tweets. Subsequently, the XGBoost-BiLSTM hybrid model harnesses this data to predict the count of infected cases. This hybrid model capitalizes on XGBoost's prowess in handling limited dataset sizes, a prevalent challenge during outbreaks with insufficient time series data. Moreover, it enriches data for BiLSTM, amplifying its efficacy in predicting and monitoring outbreaks. To construct our dataset, we extracted tweets discussing symptoms from six distinct infectious disease outbreaks (Ebola, Zika, MERS, H1N1, Chikungunya, COVID-19) spanning from 2012 to 2021. Our results demonstrate that the proposed hybrid model outperforms nine cutting-edge and baseline models. This advancement can significantly assist health authorities in minimizing fatalities and preparing preemptively for potential outbreaks. © 2023 IEEE.

关键词： Deep learning Machine learning Ontology Outbreak detection Social media Time series

来源：评论

学校读者我要写书评

暂无评论

A Rearrangement and Restore-Based Mixer Model for Target-Oriented Multimodal Sentiment Classification

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3109-3119页

作者： Jia, Li Ma, Tinghuai Rong, Huan Sheng, Victor S. Huang, Xuejian Xie, Xintong Nanjing University of Information Science and Technology School of Computer Science Nanjing Jiangsu210044 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing Jiangsu210044 China Jiangsu Ocean University School of Computer Engineering Lianyungang Jiangsu222005 China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing Jiangsu210044 China Texas Tech University Department of Computer Science LubbockTX79409 United States

With the development of fine-grained multimodal sentiment analysis tasks, target-oriented multimodal sentiment (TMSC) analysis has received more attention, which aims to classify the sentiment of target with the help of textual and associated image features. Existing methods focus on exploring fine-grained image features and incorporate transformer-based complex fusion strategies, while ignoring the heavy computational burden. Recently, some lightweight multilayer perceptrons (MLP)-based methods have been successfully applied to multimodal sentiment classification tasks. In this article, we propose an effective rearrangement and restore mixer model (RR-Mixer) for TMSC, which dedicates the interaction of image, text, and targets along the modal-axis, sequential-axis, and feature channel-axis through rearrangement and restore operations. Specifically, we take vision transformer (ViT) and robustly optimized BERT (RoBERTa) pretrained models to extract image and textual features, respectively. Further, we adopt cosine similarity to select the most semantically relevant image features. Then, an RR-Mixer module is designed for mixed multimodal features, with the core technology consisting of rolling, grouping rearrangement and restore operations. Moreover, we introduce MLP Unit to learn the information of different modalities for intermodal interaction. The results show that our model achieves superior performance on two benchmark multimodal datasets, TWITTER-15 and TWITTER-17, with a significant improvement of 4.66% and 1.26% in terms of macro-F1. © 2020 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

State-of-the-Art in Cyber-Physical Security for Dynamic Wireless Charging of Electric Vehicles

State-of-the-Art in Cyber-Physical Security for Dynamic Wire...

引用

Annual Conference of Industrial Electronics Society

作者： Samaneh Yazdanipour V. S. R. Varaprasad Oruganti Jeonggi Son Mohammadreza F. M. Arani Sheldon S. Williamson Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Dynamic Wireless Charging systems for electric vehicles offers a revolutionary approach to EV charging, enabling vehicles to charge while in motion and overcoming the limitations of stationary wireless charging. Despite their efficiency and convenience, dynamic wireless charging systems introduce complex security vulnerabilities that must be addressed to ensure system reliability and safety. This paper provides a comprehensive review of the cyber-physical security challenges associated with electric vehicle dynamic wireless charging systems, examining potential threats such as cyber attacks and physical tampering. It identifies specific vulnerabilities in communication protocols, hardware, and software, and discusses various mitigation strategies such as encryption and physical layer security enhancements. Furthermore, the paper highlights recent advancements in security technologies and outlines future research directions to enhance the resilience of dynamic wireless charging systems. By addressing these security challenges, stakeholders can foster a safer environment for the deployment of dynamic wireless charging, thereby promoting the broader adoption of electric vehicles and advancing sustainable transportation solutions.

关键词： Wireless communication technological innovation Inductive charging Dynamics Transportation Security Stakeholders Vehicle dynamics Cyberattack Resilience

来源：评论

学校读者我要写书评

暂无评论

An Anomaly Detection Model for IoT Networks based on Flow and Flag Features using a Feed-Forward Neural Network 19

An Anomaly Detection Model for IoT Networks based on Flow an...

引用

19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Ullah, Imtiaz Mahmoud, Qusay H. Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C5 Canada

ISBN: (纸本)9781665431620

The security of IoT networks is becoming increasingly challenging, and anomaly detection for IoT network traffic is a critical technique for addressing this issue. However, extracting precise and effective network traffic features for anomaly detection is challenging. To address this issue, the current research analyzes various types of network flow features. In this paper, we present the design and development of an anomalous activity detection system for IoT networks based on flow and control flags features using a feed-forward neural network. The model has been evaluated using BoT-IoT, IoT network intrusion, MQTT-IoT-IDS2020, MQTTset, IoT-23, and IoT-DS2 datasets for binary and multiclass classification. Our proposed binary and multiclass classification model attained high accuracy, precision, recall, and F1 score compared to existing deep learning implementations. © 2022 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Towards Enhancing Aviation Safety Through Advanced Incident Analysis Using Large Language Models

Towards Enhancing Aviation Safety Through Advanced Incident ...

引用

Collaborative Advances in software and COmputiNg (CASCON), International Conference on

作者： Vaishali Siddeshwar Akramul Azim Sanaa Alwidian Masoud Makrehchi Department of Electrical Computer and Software Engineering University of Ontario Institute of Technology

ISBN: (数字)9798331504830

ISBN: (纸本)9798331515621

Aerospace incident reports, crucial for identifying abnormal aircraft occurrences, are submitted to regulatory authorities like FAA or EASA. These reports, often in natural language, offer clarity and context. Existing literature proposes incident-analysis tools utilizing manual rules and NLP techniques like Part-of-Speech Tagging and dependency parsers to extract entities from logs. However, these approaches may miss nuances, leading to incomplete or inaccurate extractions and potential mis-interpretations. This study aims to automate initial extraction using Large Language Models, extracting Aircraft Number/Model, contributing and human factors, primary root cause, and incident summaries. Automating this phase can expedite investigations and improve expert efficiency during review processes.

关键词： Reviews Large language models Natural languages Human factors Manuals Tagging software Data mining Personnel Aircraft

来源：评论

学校读者我要写书评

暂无评论

Constrained Over-the-Air Model Updating for Wireless Online Federated Learning with Delayed Information

arXiv

引用

arXiv 2025年

作者： Wang, Juncheng Liu, Yituo Liang, Ben Dong, Min Department of Computer Science Hong Kong Baptist University Hong Kong Department of Electrical and Computer Engineering University of Toronto Canada Department of Electrical Computer and Software Engineering Ontario Tech University Canada

We study online federated learning over a wireless network, where the central server updates an online global model sequence to minimize the time-varying loss of multiple local devices over time. The server updates the global model through over-the-air model-difference aggregation from the local devices over a noisy multiple-access fading channel. We consider the practical scenario where information on both the local loss functions and the channel states is delayed, and each local device is under a time-varying power constraint. We propose Constrained Over-the-air Model Updating with Delayed infOrmation (COMUDO), where a new lower-and-upper-bounded virtual queue is introduced to counter the delayed information and control the hard constraint violation. We show that its local model updates can be efficiently computed in closed-form expressions. Furthermore, through a new Lyapunov drift analysis, we show that COMUDO provides bounds on the dynamic regret, static regret, and hard constraint violation. Simulation results on image classification tasks under practical wireless network settings show substantial accuracy gain of COMUDO over state-of-the-art approaches, especially in the low-power region. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Building Trust in Deep Learning Models via a Self- Interpretable Visual Architecture

Building Trust in Deep Learning Models via a Self- Interpret...

引用

Annual Conference on Privacy, Security and Trust, PST

作者： Weimin Zhao Qusay H. Mahmoud Sanaa Alwidian Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada

Deep learning models are being utilized and further developed in many application domains, but challenges still exist regarding their interpretability and consistency. Interpretability is important to provide users with transparent information that enhances the trust between the user and the learning model. It also gives developers feedback to improve the consistency of their deep learning models. In this paper, we present a novel architectural design to embed interpretation into the architecture of the deep learning model. We apply dynamic pixel-wised weights to input images and produce a highly correlated feature map for classification. This feature map is useful for providing interpretation and transparent information about the decision-making of the deep learning model while keeping full context about the relevant feature information compared to previous interpretation algorithms. The proposed model achieved 92% accuracy for CIFAR 10 classifications without finetuning the hyperparameters. Furthermore, it achieved a 20% accuracy under 8/255 PGD adversarial attack for 100 iterations without any defense method, indicating extra natural robustness compared to other Convolutional Neural Network (CNN) models. The results demonstrate the feasibility of the proposed architecture.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Robust Scheduling Algorithm for Overload-Tolerant Real-Time Systems

A Robust Scheduling Algorithm for Overload-Tolerant Real-Tim...

引用

International Symposium on Object-Oriented Real-Time Distributed Computing

作者： Amin Avan Akramul Azim Qusay H. Mahmoud Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada

A real-time system is overloaded when all the tasks in a workload cannot meet their deadlines, and hence a robust algorithm is essential to maximize the number of tasks that meet their deadlines with the minimum number of miss rates and context switching. Although the Rate Monotonic (RM), Earliest Deadline First (EDF), and Least Laxity First (LLF) algorithms optimally perform and schedule tasks on a non-overloaded system, they have deficient performance when the system is overloaded. Therefore, we propose a new scheduling algorithm for uniprocessor and partitioned multiprocessor systems to address the overload situation. Since the proposed scheduling algorithm operates like EDF non-overloaded conditions, the proposed algorithm is optimal for non-overloaded systems. In addition, the proposed algorithm is robust against overloading situations as it executes the maximum possible tasks in the overload situation instead of missing deadlines of many tasks or burdening context switching to the system. The proposed algorithm allocates a processor to tasks based on the possibility of executing the task. The experimental results demonstrate that the proposed scheduling algorithm maximizes the number of tasks that meet their deadlines in overload conditions without a domino effect and context switching. In addition, the proposed algorithm achieves the lowest miss rate without context switching and the highest efficiency and processor utilization in the overloaded system compared with RM, EDF, and LLF.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Using time-correlated noise to encourage exploration and improve autonomous agents performance in Reinforcement Learning 18

Using time-correlated noise to encourage exploration and imp...

引用

18th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information technology, SEIT 2021

作者： Peixoto, Maria J.P. Azim, Akramul Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

Intelligent autonomous agents need to know how to carry out actions based on reasoning, perception, and analysis. Therefore, reinforcement learning algorithms guide the agent to reach this goal by performing steps that guarantee the most significant reward. The problem with this approach is that when the agent finds an optimal action with a considerable premium, it tends to stop exploring the environment to guarantee only that great reward. In this way, the agent stops making a great exploration to find new ways and learn alternatives that could generate a bigger bonus in the face of a change in its context. To alleviate this problem, some techniques, such as Soft Actor-Critic (SAC) and Asynchronous Advantage Actor-Critic (A3C), use entropy regulation to improve policy optimization in reinforcement learning. Thus, the greater the entropy, the lower the probability of the agent's certainty in a given action. Despite entropy, these algorithms are not immune to local optimal and require additional exploration mechanisms. Therefore, this work proposes an approach that helps the agent's exploitation policy during reinforcement learning training and improves its performance during the test time. We use the latest state-of-the-art (SOTA) approaches, that is, Asynchronous Advantage Actor-Critic (A3C), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC) in this work. We created a time-correlated noise in the agent's policy network from the use of a random disturbance generated with the Ornstein-Uhlenbeck process. According to the experiments carried out and the results obtained, we can see that our proposal allowed the agent to explore the environment more during training and improve its performance during the testing time, increasing the reward received in different learning contexts. © 2021 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Implementation of mho characteristics in FPAA-based reconfigurable new generation static relay

Implementation of mho characteristics in FPAA-based reconfig...

引用

19th IET Conference on Developments in Power System Protection (DPSP Europe 2025)

作者： Prasoon Arya Sarasij Das Tarlochan Sidhu Department of Electrical Engineering Indian Institute of Science Bangalore India Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9781837243167

Static relays are reliable, robust and fast compared to digital relays. However, they are bulky and hard to configure. The discovery of microprocessor-based relays overcame these limitations, although they are prone to cyber-attacks as they are digital computers. It is tough to detect malicious code and malware in digital computers; conversely, analog circuits are immune to these attacks. Before microprocessor-based relays, static relays were used in power systems protection; they are still considered reliable, robust, fast and immune to cyber threats as they didn’t have any digital components. With the developments in analog technology, such as Field Programmable analog Array (FPAA), there is now an opportunity for analog relays to rebound with reduced size, ease of design and reconfigurability. This work proposes an FPAA-based new generation analog mho relay demonstrating a fully analog implementation of mho distance characteristics on FPAA. The line-ground (LG) and line-line (LL) elements are modelled in the AN231K04-QUAD4 kit and tested on the IEEE 9-Bus system. The results are promising. Relay shows dependability over the designed range of its operation. Besides, this work also proposes using FPAA-based static relays along with the existing digital relays to enhance the cyber security of the protection schemes.

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：