检索结果-内蒙古大学图书馆

An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

来源：评论

学校读者我要写书评

暂无评论

Debiasing computer Vision Models using data Augmentation based Adversarial Techniques 3

Debiasing Computer Vision Models using Data Augmentation bas...

引用

3rd Workshop on Bias, Risk, Ethical AI, Explainability and the Role of Logic and Logic Programming, BEWARE 2024

作者： Kumar, Teerath Mandal, Abhishek Leavy, Susan Little, Suzanne Mileo, Alessandra Bendechache, Malika CRT-AI School of Computing Dublin City University Dublin Ireland Insight SFI Research Center for Data Analytics School of Computing Dublin City University Dublin Ireland Insight SFI Research Center for Data Analytics School of Information and Communication Studies University College Dublin Dublin Ireland ADAPT & Lero Research Centres School of Computer Science University of Galway Galway Ireland

Deep learning models in computer vision, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have been found to exhibit significant biases related to factors such as gender and ethnicity. These biases often originate from inherent imbalances in training data predominantly sourced from the internet. In this study, we aim to address gender bias in computer vision models by curating a specialized dataset that highlights gender-related disparities. Additionally, we measure dataset diversity across six datasets (FFHQ, WIKI, IMDB, LFW, UTK Faces, diverse dataset), five professions (CEO, engineer, nurse, politician, and teacher) and different query retrieval tasks using the Image Similarity Score (ISS). To reduce learned gender biases and increase data diversity, we propose adversarial data augmentation techniques that specifically target facial regions within images. These techniques, named Partial Mix (PM), that partially mixes two gendered faces in a squared pattern, and Noise Addition (NA), that adds noise to the facial region, are designed to mitigate bias. Our experimental results demonstrate increased data diversity across the six datasets and professions, along with reduction in gender bias for CNN-based models. However, these adversarial techniques were less effective in reducing bias for Vision Transformers. This discrepancy highlights the unique challenges for bias mitigation posed by ViTs. Consistent with prior research, our findings indicate that ViTs learn from a broader set of visual cues compared to CNNs. This increased sensitivity makes ViTs more prone to amplifying biases, emphasizing the need for tailored bias mitigation strategies when deploying these models in real-world applications. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

DIGITAL PHRENOLOGY: ALGORITHMS AND ETHICS FROM A SYSTEMS PERSPECTIVE 67

DIGITAL PHRENOLOGY: ALGORITHMS AND ETHICS FROM A SYSTEMS PER...

引用

67th Annual Meeting of the International Society for the Systems sciences, ISSS 2023

作者： Keykaan, Dimitrius Snyman, Dirk Kruger, Hennie Verhoef, Anné Unit for Data Science and Computing North-West University South Africa School of Philosophy North-West University South Africa Department of Information Systems University of Cape Town South Africa

ISBN: (纸本)9781713893455

Decisions made by machine learning algorithms have societal impacts, both good and bad. This study acknowledges the great good that machine learning algorithms contribute to society while also pointing out that there are negative impacts such as bias, discrimination and misrepresentation of data, etc. Algorithms often use either irrelevant or incomplete data to make life-changing determinations about certain groups of society. The algorithmic use of data that does not represent the true or actual ability of individuals or groups of people leads to digital phrenology. When algorithms are designed and tested outside the real world, it may not be obvious that digital phrenology may be caused. The ethical consequences of this problem can be mitigated if algorithms are not viewed as separate from the world it is eventually deployed into. Therefore, a more holistic systems view of algorithms, including their ethical consequences, should be taken. The purpose of this paper is to show that digital phrenology exists and that, once deployed, algorithms become part of a societal system where ethical values cannot be ignored. Various efforts and approaches to mitigate the ethical concerns related to unethical algorithmic behaviour have either done well in raising awareness or produced promising results. However, very little attention has been given to the explicit use of ethical theories as a possible approach to alleviating the problem. Therefore, two major ethical theories are proposed to assess the ethicality of hypothetical machine learning scenarios. A critical analysis of related literature and case studies is central in this study's inquiry method. Results show the existence of digital phrenology. It also reveals how viewing algorithms as a single component of a larger societal system can give a better perspective on the possible ethical consequences that arise from their use. This research contends that the use of ethical theories can contribute to addressing digital ph

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Tensor Graph Convolutional Network for Dynamic Graph Representation Learning 7

Tensor Graph Convolutional Network for Dynamic Graph Represe...

引用

7th International Symposium on Autonomous Systems, ISAS 2024

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application For Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9798350363173

Dynamic graphs (DG) represent evolving interactions between entities in various real-world scenarios. Many existing DG representation learning models employ a combination of graph convolutional networks and sequence neural networks to capture spatial-temporal dependencies. However, this hybrid approach often fails to effectively capture the spatial-temporal continuity inherent in DGs. In this paper, we propose a novel approach called the Tensor Graph Convolutional Network (TGCN) to learn DG representations within a unified convolution framework based on tensor products. Our approach is founded on two key principles: (a) representing DG information in tensor form, and (b) leveraging tensor product operations to design a graph convolutional network that simultaneously models spatial and temporal features. Experimental results on three real-world DG datasets demonstrate that our proposed model achieves state-of-the-art performance. © 2024 IEEE.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

DE-ConvGraph 3D UNet: A Novel Deep Learning Model for Optimizing Radiotherapy Treatment Plans in Oropharyngeal Cancer

DE-ConvGraph 3D UNet: A Novel Deep Learning Model for Optimi...

引用

2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Murugadoss, Bhuvanashree Amudha, J. Sugumaran, Vijayan Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science and Engineering Karnataka Bengaluru India Oakland University Center for Data Science and Big Data Analytics RochesterMI United States Oakland University Department of Decision and Information Sciences RochesterMI United States

ISBN: (纸本)9798350359527

Automated radiotherapy treatment planning aims to improve treatment accuracy and efficiency. However, the prevalent Knowledge-Based Planning (KBP) method faces issues like lengthy manual problem formulation and challenges in accurately modeling human anatomy and processing high-dimensional data. This work focuses on treatment plan optimization and considers deep learning as a potential solution. Deep learning algorithms, by virtue of their ability to learn from large quantities of data and model complex relationships, can automate the formulation of the optimization problem in KBP, saving significant time and effort. However, despite these compelling advantages, it should be noted that the current convolution-based encoder-decoder models used for radiotherapy treatment plan optimization have a limited capability in capturing long-range dependencies between distant voxels. This work aims to introduce 'DE-ConvGraph 3D UNet', a novel deep learning model, to address these limitations and optimize radiotherapy treatment plans for oropharyngeal cancer. The proposed 'DE-ConvGraph 3D UNet' model includes a Graph Convolutional Network (GCN) component to capture long-range dependencies between distant voxels. Furthermore, the dual-encoder structure of the model combines the strengths of GCN and 3D Convolutional U-Net, enabling global relationships and local pattern capturing in a 3D patient volume. Experiments were conducted using the benchmark dataset-OpenKBP-Opt. The proposed model shows improvements in performance in comparison to state-of-The-Art U-Net variants in terms of mean squared voxel-wise error, dose volume histogram points and clinical criteria satisfaction. © 2023 IEEE.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Task Separation and Knowledge Sharing for Class Incremental Learning 5

Task Separation and Knowledge Sharing for Class Incremental ...

引用

5th International Conference on Intelligent computing and Human-computer Interaction, ICHCI 2024

作者： Zhang, Jiali Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China

ISBN: (纸本)9798350368284

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

UAV Deployment Optimization for Efficient data Forwarding in UAV-assisted Wireless Networks

UAV Deployment Optimization for Efficient Data Forwarding in...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Xueqi Wang, Aimin Sun, Geng Liu, Lingling Zhang, Jing Wang, Jiacheng Shi, Wenxiao Jilin University College of Computer Science and Technology Changchun130012 China Hainan University School of Information and Communication Engineering Haikou570000 China Jilin University College of Communication Engineering Changchun130012 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

ISBN: (纸本)9798350351255

Given that the locations of base stations (BSs) remain fixed after installation, direct data forwarding to remote user equipment (UE) becomes challenging. Unmanned aerial vehicles (UAVs) offer a hopeful solution as mobile relays for next generation wireless communications to realize data forwarding with the flexible and cost-effective deployment. However, the limited onboard energy of UAVs and slow progress in energy storage technology pose significant challenges to achieving energy-efficient communication. Therefore, in this article, we investigate a wireless communication network utilizing a UAV as a high-altitude relay for data forwarding, and formulate a UAV relay deployment optimization problem (URDOP) to minimize the energy consumption of data forwarding and UAV hovering by optimizing UAV deployment, including the locations and number of UAV hover points. Given that the URDOP is a mixed-integer programming problem, conventional gradient-based approaches face limitations. To address this, we propose a self-adaptive differential evolution with a variable population size (SaDEVPS) algorithm to solve the URDOP. The performance of proposed SaDEVPS is verified through simulations, and the results show that it can successfully decrease the energy consumption of system when compared to other benchmark algorithms. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Blockchain-based Searchable Encryption Scheme for Spatial Text data 13

Blockchain-based Searchable Encryption Scheme for Spatial Te...

引用

13th International Conference on Communication and Network Security, ICCNS 2023

作者： Lin, Zhirun Yang, Yang Zhan, Yonghua Li, Jiayin Dong, Chen College of Computer and Data Science Fuzhou University Fujian Fuzhou China School of Computing and Information Systems Singapore Management University Singapore Singapore College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou China

ISBN: (纸本)9798400707964

In today's society, an increasing number of users and enterprises encrypt substantial amounts of data, including spatial text data, and outsource it to cloud servers. However, efficiently and securely querying encrypted spatial text data remains a pressing issue. In this article, we propose a blockchain-based searchable encryption scheme for spatial text data. Users encrypt documents using methods such as order-preserving encryption and upload them to the blockchain for on-chain storage. The cloud server can only retrieve information stored within the blocks, facilitating keyword and coordinate comparisons. To enhance query speed, we introduce a sliding window that allows the server to quickly skip through non-matching blocks. Finally, through experiments and comparisons, this paper demonstrates that the proposed scheme excels in query efficiency and computational overhead, proving its superiority and feasibility compared to other solutions. © 2023 ACM.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

引用

Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

RAGRAPH: A General Retrieval-Augmented Graph Learning Framework 38

RAGRAPH: A General Retrieval-Augmented Graph Learning Framew...

引用

38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Jiang, Xinke Qiu, Rihong Xu, Yongxin Zhang, Wentao Zhu, Yichen Zhang, Ruizhe Fang, Yuchen Chu, Xu Zhao, Junfeng Wang, Yasha School of Computer Science Peking University China University of Electronic Science and Technology of China China Center on Frontiers of Computing Studies Peking University Beijing China Big Data Technology Research Center Nanhu Laboratory Jiaxing China Peking University Information Technology Institute Binhai Tianjin China

Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGRAPH), which brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. On the top of our framework is a toy graph vector library that we established, which captures key attributes, such as features and task-specific label information. During inference, the RAGRAPH adeptly retrieves similar toy graphs based on key similarities in downstream tasks, integrating the retrieved data to enrich the learning context via the message-passing prompting mechanism. Our extensive experimental evaluations demonstrate that RAGRAPH significantly outperforms state-of-the-art graph learning methods in multiple tasks such as node classification, link prediction, and graph classification across both dynamic and static datasets. Furthermore, extensive testing confirms that RAGRAPH consistently maintains high performance without the need for task-specific fine-tuning, highlighting its adaptability, robustness, and broad applicability. © 2024 Neural information processing systems foundation. All rights reserved.

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：