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V2I Based Environment Perception for Autonomous Vehicles at Intersections

China Communications 2021年第7期18卷 1-12页

作者： Xuting Duan Hang Jiang Daxin Tian Tianyuan Zou Jianshan Zhou Yue Cao Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems&Safety ControlSchool of Transportation Science and EngineeringBeihang UniversityBeijing 100191China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China

In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges.V2I(Vehicle-to-Infrastructure)communication is a potential solution to enable cooperative intelligence of vehicles and *** this paper,the RGB-PVRCNN,an environment perception framework,is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication *** framework integrates vision feature based on *** normal distributions transform(NDT)point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the *** field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.

关键词： V2I environmental perception autonomous vehicles 3D objects detection

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Universal and Efficient Adversarial Training Framework with Membership Inference Resistance

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IEEE Internet of Things Journal 2025年第12期12卷 18665-18677页

作者： Yan, Ran Du, Ruiying He, Kun Chen, Jing Li, Qiao Wu, Cong The Collaborative Innovation Center of Geospatial Technology School of Cyber Science and Engineering Wuhan University Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Shenzhen Institute of Wuhan University China Nanyang Technological University College of Computing and Data Science Singapore Singapore

Adversarial training is an effective approach to enhance the robustness of machine learning models via adding adversarial examples into the training phase. However, existing adversarial training methods increase the advantage of membership inference attacks, which aim to determine from the model whether an example is in the training dataset. In this paper, we propose an adversarial training framework that guarantees both robustness and membership privacy by introducing a tailor-made example called reverse-symmetry example. Moreover, our framework reduces the number of required adversarial examples compared with existing adversarial training methods. We implement our framework using four adversarial training methods on the FMNIST and CIFAR10 datasets and compare its performance with deep learning differential privacy. Our experimental findings demonstrate that our framework mitigates model overfitting and outperforms the original adversarial training with respect to the overall performance of accuracy, robustness, privacy, and runtime. © 2014 IEEE.

关键词： Adversarial machine learning

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Detection of Brain Tumor from MRI Images Using VGG-16 CNN Model with Transfer Learning

Detection of Brain Tumor from MRI Images Using VGG-16 CNN Mo...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： Bhanu Pratap Singh Rathore Rutul Dave Dhanasekaran K Priyadarsini K Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposure to radiation, or environmental factors. Early detection of brain tumors is essential for effective treatment and improved patient outcomes. To address this, a novel approach has been proposed for brain tumor detection using transfer learning with the VGG-16 convolutional neural network (CNN) architecture. By leveraging the pre-trained VGG-16 model on the ImageNet dataset, which provides a broad range of data for learning and representing complex features of various objects, meaningful features can be extracted from MRI scans and differentiate between tumor and normal tissue accurately. This refined VGG-16 model can provide a more precise and reliable diagnosis, which is crucial for successful treatment. This research study demonstrates that the utilization of VGG-16 model for brain tumor diagnosis can provide an efficient and accurate method for identifying brain tumors. Moreover, this approach can be applied to develop automated systems to assist medical professionals in diagnosing and treating brain tumors more effectively.

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Privacy-preserving graph publishing with disentangled variational information bottleneck

Privacy-preserving graph publishing with disentangled variat...

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作者： Ma, Liya Li, Chen Sun, Siqi Guo, Shu Wang, Lihong Li, Jianxin School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China ARC LAB Tencent Shenzhen China China Academy of Industrial Internet Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

Social networks collect enormous amounts of user personal and behavioral data, which could threaten users' privacy if published or shared directly. Privacy-preserving graph publishing (PPGP) can make user data available while protecting private information. For this purpose, in PPGP, anonymization methods like perturbation and generalization are commonly used. However, traditional anonymization methods are challenging in balancing high-level privacy and utility, ineffective at defending against both various link and hybrid inference attacks, as well as vulnerable to graph neural network (GNN)-based attacks. To solve those problems, we present a novel privacy-disentangled approach that disentangles private and non-private information for a better privacy-utility trade-off. Moreover, we propose a unified graph deep learning framework for PPGP, denoted privacy-disentangled variational information bottleneck (PDVIB). Using low-dimensional perturbations, the model generates an anonymized graph to defend against various inference attacks, including GNN-based attacks. Particularly, the model fits various privacy settings by employing adjustable perturbations at the node level. With three real-world datasets, PDVIB is demonstrated to generate robust anonymous graphs that defend against various privacy inference attacks while maintaining the utility of non-private information. © 2023 John Wiley & Sons Ltd.

关键词： Graph neural networks

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Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality datasets with High Missing data Rates

Comparative Analysis of Machine Learning-Based Imputation Te...

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Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES), IEEE Symposia on

作者： Sen Yan David J. O’Connor Xiaojun Wang Noel E. O’Connor Alan. F. Smeaton Mingming Liu School of Electronic Engineering and Insight Research Ireland Centre for Data Analytics Dublin City University Dublin Ireland School of Chemical Sciences Dublin City University Dublin Ireland School of Electronic Engineering and the Entwine Centre Dublin City University Dublin Ireland School of Computing and Insight Research Ireland Centre for Data Analytics Dublin City University Dublin Ireland

ISBN: (数字)9798331508258

ISBN: (纸本)9798331508265

Urban pollution poses serious health risks, particularly in relation to traffic-related air pollution, which remains a major concern in many cities. Vehicle emissions contribute to respiratory and cardiovascular issues, especially for vulnerable and exposed road users like pedestrians and cyclists. Therefore, accurate air quality monitoring with high spatial resolution is vital for good urban environmental management. This study aims to provide insights for processing spatiotemporal datasets with high missing data rates. In this study, the challenge of high missing data rates is a result of the limited data available and the fine granularity required for precise classification of PM 2.5 levels. The data used for analysis and imputation were collected from both mobile sensors and fixed stations by Dynamic Parcel Distribution, the Environmental Protection Agency, and Google in Dublin, Ireland, where the missing data rate was approximately 82.42%, making accurate Particulate Matter 2.5 level predictions particularly difficult. Various imputation and prediction approaches were evaluated and compared, including ensemble methods, deep learning models, and diffusion models. External features such as traffic flow, weather conditions, and data from the nearest stations were incorporated to enhance model performance. The results indicate that diffusion methods with external features achieved the highest F1 score, reaching 0.9486 (Accuracy: 94.26%, Precision: 94.42%, Recall: 94.82%), with ensemble models achieving the highest accuracy of 94.82%, illustrating that good performance can be obtained despite a high missing data rate.

关键词： Accuracy Urban pollution Atmospheric modeling Urban areas Diffusion models Air pollution Imputation data models Spatiotemporal phenomena Vehicle dynamics

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DarkSAM: Fooling Segment Anything Model to Segment Nothing

arXiv

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arXiv 2024年

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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SumPA: Efficient Pattern-Centric Graph Mining with Pattern Abstraction 21

SumPA: Efficient Pattern-Centric Graph Mining with Pattern A...

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Proceedings of the 30th International Conference on Parallel Architectures and Compilation Techniques

作者： Chuangyi Gui Xiaofei Liao Long Zheng Pengcheng Yao Qinggang Wang Hai Jin National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665442787

Graph mining aims to explore interesting structural information of a graph. Pattern-centric systems typically transform a generic-purpose graph mining problem into a series of subgraph matching problems for high performance. Existing pattern-centric mining systems reduce the substantial search space towards a single pattern by exploring a highly-optimized matching order, but inherent computational redundancies of such a matching order itself still suffer severely, leading to significant performance degradation. The key innovation of this work lies in a general redundancy criterion that characterizes computational redundancies arising in not only handing a single pattern but also matching multiple patterns simultaneously. In this paper, we present SumPA, a high-performance pattern-centric graph mining system that can sufficiently remove redundant computations for any complex graph mining problems. SumPA features three key designs: (1) a pattern abstraction technique that can simplify numerous complex patterns into a few simple abstract patterns based on pattern similarity, (2) abstraction-guided pattern matching that completely eliminates (totally and partially) redundant computations during subgraph enumeration, and (3) a suite of system optimizations to maximize storage and computation efficiency. Our evaluation on a wide variety of real-world graphs shows that SumPA outperforms the two state-of-the-art systems Peregrine and GraphPi by up to 61.89× and 8.94×, respectively. For many mining problems on large graphs, Peregrine takes hours or even days while SumPA finishes in only a few minutes.

关键词： data reuse

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Leveraging SDN: Empirical Evaluation of Privacy Enriched data Communication over VANET Using Software Defined Network Assistance 9

Leveraging SDN: Empirical Evaluation of Privacy Enriched Dat...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Ratheesh, R. Vijayalakshmi, S. Arunsundar, B. Swarna, M. Saranya, G. Agni College of Technology Department of Ece Chennai India R.M.K. Engineering College Department of Ece Chennai India School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Kattankulathur India Dr. M.G.R. Educational And Research Institute University Department of Ece Chennai India Sri Krishna College of Engineering And Technology Department of Electronics And Communication Engineering Coimbatore Kuniyamuthur India

ISBN: (纸本)9798350365092

In the realm of modern transportation systems, Vehicular Ad Hoc Networks (VANETs) play a pivotal role in enabling efficient communication among vehicles and infrastructure elements for enhanced road safety and traffic management. However, the inherent vulnerabilities in VANETs, such as privacy breaches and security threats, necessitate robust frameworks to mitigate risks and ensure secure data transmission. This paper presents a comprehensive architecture for Privacy-Enriched data Communication over VANETs using Software-Defined Network (SDN) assistance. The proposed framework integrates key components including vehicles, Roadside Units (RSUs), and SDN controllers to facilitate secure and privacy-aware communication. Vehicles act as data sources, employing pseudonyms and encryption mechanisms to protect user privacy. RSUs serve as access points, collecting and relaying data while enforcing access control policies. SDN controllers provide centralized management, dynamically allocating resources and enforcing privacy policies through encryption, pseudonymization, and access control mechanisms. Communication protocols such as IEEE 802.11p and LTE enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, while the OpenFlow protocol facilitates communication between SDN controllers and network devices. Additionally, trust management mechanisms evaluate the trustworthiness of network entities based on behavioral analysis, reliability metrics, and security considerations, ensuring the integrity and resilience of the network. By amalgamating SDN-based management with privacy enhancement mechanisms and trust management protocols, the proposed framework offers a robust solution for addressing privacy and security challenges in VANETs, thereby fostering safer and more secure transportation ecosystems. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Smart Signaling: A Smart Internet of Things Assisted Traffic Light Controlling and Monitoring System using Intelligent Sensors 9

Smart Signaling: A Smart Internet of Things Assisted Traffic...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Saranya, G. Ratheesh, R. Vijayalakshmi, S. Arunsundar, B. Swarna, M. Sri Krishna College of Engineering And Technology Department of Electronics And Communication Engineering Coimbatore Kuniyamuthur India Agni College of Technology Department of Ece Chennai India R.M.K. Engineering College Department of Ece Chennai India School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Kattankulathur India Dr. M.G.R. Educational And Research Institute University Department of Ece Chennai India

ISBN: (纸本)9798350365092

In contemporary urban environments, efficient traffic management stands as a paramount challenge, necessitating innovative solutions to mitigate congestion, enhance safety, and reduce environmental impact. This paper proposes a Smart Internet of Things (IoT)-assisted Traffic Light Controlling and Monitoring System designed to revolutionize traffic management through real-time data acquisition, analysis, and dynamic control mechanisms. The system integrates edge devices equipped with a variety of sensors, cloud-based infrastructure, and intelligent algorithms to optimize traffic flow at intersections. Leveraging technologies such as AWS IoT, Lambda, and QuickSight, alongside communication protocols like MQTT and HTTP, the system enables seamless device management, secure data transmission, and scalable real-time analytics. Hardware components including microcontrollers, sensors, and actuators facilitate precise data collection, allowing for accurate traffic monitoring and adaptive signal control. Through comprehensive traffic flow analysis, congestion reduction strategies, environmental impact assessments, safety enhancements, and system performance optimizations, the proposed system aims to revolutionize urban traffic management, leading to smoother traffic flow, reduced congestion, improved safety, and enhanced environmental sustainability. © 2024 IEEE.

关键词： Internet of things

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Singe Image Dehazing using Deep Neural Networks

Singe Image Dehazing using Deep Neural Networks

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Nisarg Doshi Sagar Bhavsar Rajeswari D Srinivasan R Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear *** first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r.t SSIM values.

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