检索结果-内蒙古大学图书馆

Proceedings of the Winter Simulation Conference

作者： Aidan Fahlman Gabriel Wainer Systems and Computer Engineering Carleton University Ottawa ON CANADA

ISBN: (纸本)9798350369663

Compartmental models have been utilized in the study and understanding of the COVID-19 pandemic. Traditional models have been expanded to include geographical level transmission dynamics and new states. Here, we present a model based on Cell-DEVS specifications that can be used to define and study the effects of basic human behavior. We include mask wearing and lockdown fatigue, and an adaptable framework allowing for the rapid prototyping of different diseases and behaviors. We exemplify how to build the model and adapt the attributes using the provinces of Canada as a case study. The results show the effect mask mandates, mask wearing, and lockdown fatigue have on case counts over time.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Interplay of Attacker Behaviors and Dependability Attributes in Industrial Control System Impact Analysis

Interplay of Attacker Behaviors and Dependability Attributes...

引用

International Conference on Dependable systems and Their Applications (DSA)

作者： Alvi Jawad Jason Jaskolka Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798331532390

ISBN: (纸本)9798331532406

Industrial control systems (ICS) are increasingly vulnerable to cyberattacks that can propagate to impact physical industrial processes. Existing research on ICS impact analysis views ICS dependability attributes as an afterthought and focuses primarily on attacks but not the attackers and their different behaviors. In this work, we include explicit considerations for ICS dependability attributes and attacker behaviors in ICS impact analysis. By adopting the Structured Cyberattack Impact Analysis (SCIA) approach, our model-based impact analysis is demonstrated on a manufacturing ICS modeled in UPPAAL-SMC. More specifically, we visualize and quantify, respectively, using simulations and statistical model checking, the potential impact of data tampering attacks when performed by attackers with different behaviors (random, relentless, and informed). Furthermore, the impact analysis results highlight the interplay of ICS dependability attributes in terms of (1) how attacks on ICS security can impact system reliability and availability, (2) how improving security can improve system availability and reliability, and (3) how accepting some sacrifices on one attribute (availability) can end up improving other attributes (security and reliability).

关键词： Analytical models Process control Data visualization Model checking Data models Manufacturing Security Reliability computer crime Integrated circuit modeling

来源：评论

学校读者我要写书评

暂无评论

A Framework Using Federated Learning for IoT-Based Forest Fire Prediction

A Framework Using Federated Learning for IoT-Based Forest Fi...

引用

2023 IEEE International Conference on Internet of Things and Intelligence systems, IoTaIS 2023

作者： Purcell, Richard Naik, Kshirasagar Lung, Chung-Horng Zaman, Marzia Sampalli, Srinivas Mutakabbir, Abdul Dalhousie University Faculty of Computer Science HalifaxNS Canada Electrical and Computer Engineering University of Waterloo WaterlooON Canada Systems and Computer Engineering Carleton University OttawaON Canada Research and Development Cistel Technology OttawaON Canada

ISBN: (纸本)9798350319040

Forest fires are a growing threat to human commu-nities. The Canadian Wildland Fire Information System gives realtime information to fire management agencies and the public. However, machine learning use for forest fire ignition classification prediction within the platform and ones like it, is yet to be fully realized. We propose a novel framework that uses federated machine learning combined with Internet of Things technologies, for forest fire ignition classification prediction. The framework incorporates distributed IoT weather stations deployed in an area prone to forest fires. We find comparable prediction accuracy between a federated machine learning system and a central server machine learning system. Our federated system, trained on an imbalanced dataset comprising 5,008,365 non-ignition cases and 45,411 ignition instances, has shown encouraging outcomes. It attained an Accuracy of around 0.76 and a ROC-AUC of about 0.80. The performance is on par with other systems, indicating that our approach is effective in classifying forest fire ignitions with a spatial resolution markedly superior to that of centralized systems. © 2023 IEEE.

关键词： Deforestation

来源：评论

学校读者我要写书评

暂无评论

A Data Integration Framework with Multi-Source Big Data for Enhanced Forest Fire Prediction

A Data Integration Framework with Multi-Source Big Data for ...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Kaur, Parveen Naik, Kshirasagar Purcell, Richard Sampalli, Srinivas Lung, Chung-Horng Zaman, Marzia Mutakabbir, Abdul University of Waterloo Electrical and Computer Engineering WaterlooON Canada Dalhousie University Faculty of Computer Science HalifaxNS Canada Carleton University Systems and Computer Engineering OttawaON Canada Research and Development Cistel Technology OttawaON Canada

ISBN: (纸本)9798350324457

Forest fires pose imminent threats to ecosystems and human lives, necessitating precise prediction for effective mitigation. The challenges include managing extensive big data and addressing data imbalance. This study introduces a data integration framework that integrates data from remote sensing satellites, ground-based weather stations, and other sources to create a comprehensive weather database spanning 18 years in Alberta, Canada. Machine learning methods, including Random Forest, eXtreme Gradient Boosting, and Multi-Layer Perceptron are employed to evaluate forest fire prediction performance, overcoming the challenge of data imbalance through changes in spatial resolution, spatio-subsamping, and downsampling techniques. XGBoost exhibits results with an ROC-AUC score of 87.2% and a sensitivity of 75%.Using meteorological data and fire history improves prediction, demonstrating big data and machine learning's role in addressing forest fire challenges. © 2023 IEEE.

关键词： Downsampling Forest Fire Imbalanced data Machine Learning Random Forest XGBoost

来源：评论

学校读者我要写书评

暂无评论

Design and Development of PainBit: a Portable Device for Supporting Patients with Chronic Pain to Log their Pain

arXiv

引用

arXiv 2024年

作者： Saleem, Arsh Langstone, Beck Ouskine, Alicia Rajabiyazdi, Fateme Department of Systems and Computer Engineering Carleton University Canada Department of Human-Computer Interaction Carleton University Canada

Recently, we have seen growing interest among patients with chronic conditions to track their health-related data. There are many wearable devices available to track different health data. However, tracking pain is mostly done by using pen and paper or mobile apps. In collaboration with a healthcare professional we designed a portable pain tracker, PainBit. To gain an understanding of patients’ perspectives on our tracker, we conducted two case studies with patients living with chronic pain. We asked patients to use PainBit for two weeks and later conducted semi-structured interviews with them. Patients found PainBit useful for tracking their pain and they preferred using a physical device, PainBit, to track their pain over using a mobile phone. Patients suggested reducing the size and weight of PainBit in the next iterations. We report on the lessons learnt through our design process and the evaluation studies. © 2024, CC BY.

关键词： Visualization

来源：评论

学校读者我要写书评

暂无评论

Secure Communication With UAV-Enabled Aerial RIS: Learning Trajectory With Reflection Optimization

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2023年 1-10页

作者： Tang, Xiao Jiang, Tianqi Liu, Jinxin Li, Bin Zhai, Daosen Yu, F. Richard Han, Zhu School of Electronics and Information Northwestern Polytechinical University Xi'an China China Helicopter Research and Development Institute Jingdezhen China School of Mechanical Engineering Xi'an Jiaotong University Xi'an China School of Electronics and Information Northwestern Polytechnical University Xi'an China Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Department of Electrical and Computer Engineering University of Houston Houston USA

Reconfigurable intelligent surfaces (RISs) have manifested huge potential in enhancing information security by actively intervening the wireless propagation, yet the security gain may still be limited depending on the RIS deployment. In this paper, we propose to employ an unmanned aerial vehicle (UAV) mounting a RIS to enable on-demand reflection, noted as an aerial RIS (ARIS). The ARIS is then exploited to assist the anti-eavesdropping communications established through a conventional fixed-deployed RIS to further enhance the wireless secrecy. The secure communication is investigated by jointly optimizing the reflection at both RISs as well as the trajectory of the ARIS to maximize the average secrecy rate during the flight. To facilitate effective algorithm design, the formulated security problem is decomposed and solved in a double-layer framework. The outer layer tackles the flying trajectory through deep reinforcement learning while the inner layer solves for reflection phase shift design with manifold optimization. Finally, simulation results demonstrate the learned trajectory in various topologies as well as the superior performance of our proposal in terms of security provisioning. IEEE

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Co-Designing Unstructured Text Data Visualization systems

arXiv

引用

arXiv 2024年

作者： Langstone, Beck Rajabiyazdi, Fateme Department of Human-Computer Interaction Carleton University Canada Department of Systems and Computer Engineering Carleton University Canada

We present our in-progress work on co-designing a visualization tool for presenting unstructured text. We have conducted a focus group with a variety of professionals who regularly analyze large corpora of unstructured text. Our preliminary insights indicate there is an unmet need to visually explore the dynamics between entities and actors extracted from unstructured text. Additionally, large corpora contain multiple perspectives on the same series of events. There is a need to disentangle these perspectives and visually show the multiple narratives present in the data. In our future work, we will co-design low-fidelity prototypes to create a broad consideration space of possible solutions for visualizing unstructured text. © 2024, CC BY.

关键词： Data visualization

来源：评论

学校读者我要写书评

暂无评论

A Reinforcement-Learning Self-Healing Approach for Virtual Network Function Placement 36

A Reinforcement-Learning Self-Healing Approach for Virtual N...

引用

36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Carleton University Dept. of Systems and Computer Eng. Ottawa Canada Dept. of Software Engineering and It Montréal Canada

ISBN: (纸本)9781665477161

Modern networking paradigms like Service Function Chaining (SFC) allow for services to be broken down to a series of ordered and interconnected Virtualized Network Functions (VNFs) that can be hosted in generic servers in EdgeCloud datacenters. Nonetheless, a critical issue arises, when a hardware or software failure occurs and the VNFs of an SFC need to be repositioned, allowing to autonomously bring the system back to its normal operation, a process called self-healing. In this paper, a distributed methodology is proposed that aims to address this challenge, considering the requirements of all involved actors. Specifically, a Reinforcement Learning (RL) based algorithm is proposed that allows to iteratively optimize and determine an SFC healing solution upon a datacenter failure. As a second stage, a revenue-driven resource allocation mechanism is integrated, to resolve the contention for resources in an already functional datacenter that potentially occurs due to the repositioning. Various simulation scenarios prove the efficiency of our proposed resilient healing mechanism. © 2023 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Designing interactive data visualizations representing recovery progress for patients after stroke

arXiv

引用

arXiv 2024年

作者： Ouskine, Alicia Chan, Adrian D.C. Rajabiyazdi, Fateme Department of Human-Computer Interaction Carleton University Canada Department of Systems and Computer Engineering Carleton University Affiliate Investigator Bruyère Research Institute Canada Department of Systems and Computer Engineering Carleton University Canada

Stroke is one of the leading causes of disability worldwide. The efficacy of recovery is determined by a variety of factors, including patient adherence to rehabilitation programs. One way to increase patient adherence to their rehabilitation program is to show patients their progress that is visualized in a simple and intuitive way. We begin to gather preliminary information on Functional Capacity, Motor Function, and Mood/cognition from occupational Therapists at the Bruyere Hospital to gain a better understanding of how stroke recovery data is collected within in-patient stroke rehabilitation centers. The future aim is to design, develop, and evaluate a data visualization tool representing progress made by patients recovering from stroke. © 2024, CC BY-NC-ND.

关键词： Data visualization

来源：评论

学校读者我要写书评

暂无评论

Learning a Safe Driving Policy for Urban Area from Real-World Data

Learning a Safe Driving Policy for Urban Area from Real-Worl...

引用

ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Adil Mahmud Changcheng Huang Department of Systems and Computer Engineering Carleton University Ottawa ON Canada

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

Driving safely on the urban roads is a major impediment in achieving level 5 autonomy. To achieve this, two main streams of approaches have been proposed: module-based and end-to-end. Module based solutions try to solve the problem by dividing the whole task of driving into separate modules and solving each one at a time. On the other hand, end-to-end approaches try to provide the control command directly from the sensor data input, like what a human driver does. Deep reinforcement learning (DRL) is one of the algorithm families that has received much attention recently to achieve end-to-end solutions. As this approach is challenging, almost all the related works use simulator generated data for training a policy network. However, synthetic data does not capture the complexity, variability, realism, and diversity of the real-world environment. A reinforcement learning (RL) policy trained on synthetic dataset necessarily makes it unreliable in real-world deployment. In this study, we propose an actor-critic DRL model to learn a driving policy from a real-world urban driving dataset. The policy enables the RL agent to keep safe distance from the leading vehicle, follow traffic light, and prevents the agent from going off-road. To optimize the policy we use proximal policy optimization (PPO), a state-of-the-art reinforcement learning algorithm. Simulation results show that the agent learns some of the basic safe driving requirements effectively.

关键词： Training Simulation Roads Urban areas Deep reinforcement learning Task analysis Streams

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：