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Real-Time Road Damage Detection and Infrastructure Evaluation Leveraging Unmanned Aerial Vehicles and Tiny Machine Learning

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IEEE Internet of Things Journal 2024年第12期11卷 21347-21358页

作者： Khan, Muhammad Waseem Obaidat, Mohammad S. Mahmood, Khalid Batool, Dania Badar, Hafiz Muhammad Sanaullah Aamir, Muhammad Gao, Wu Comsats University Islamabad Sahiwal Campus Department Of Computer Science Sahiwal57000 Pakistan Superior University Department Of Computer Science Lahore54000 Pakistan The University Of Jordan King Abdullah Ii School Of Information Technology Amman11942 Jordan University Of Science And Technology Beijing School Of Communication And Computing Engineering Beijing100083 China Amity University Amity School Of Engineering And Technology Uttar Pradesh Noida201301 India National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan University Of Palermo Department Of Engineering Palermo90133 Italy Emerson University Department Of Emerging Computing Technologies Multan60000 Pakistan Northwestern Polytechnical University Center For Bio-Inspired System-on-Chip School Of Computer Science Xi'an710060 China

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages, such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 data set collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors: Faster R-CNN ResNet101 and one-stage detectors: SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mean average precision (mAP) of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance. © 2014 IEEE.

关键词： Object detection

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AFaVS: Accurate Yet Fast Version Switching for Graph Processing Systems 39

AFaVS: Accurate Yet Fast Version Switching for Graph Process...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Zheng, Long Ye, Xiangyu Liu, Haifeng Wang, Qinggang Huang, Yu Gui, Chuangyi Yao, Pengcheng Liao, Xiaofei Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Laboratory Wuhan430074 China Zhejiang Lab Hangzhou311121 China Unsw School of Computer Science and Engineering Sydney Australia

ISBN: (纸本)9798350322279

Multi-version graph processing has been widely used to solve many real-world problems. The process of the multi-version graph processing typically includes: (1) a history graph version switching at a specific time and (2) graph processing on this history graph. Existing multi-version graph systems assume ideally that every request for a particular graph version at a particular time will have a corresponding snapshot available. However, in most cases, this is not true. Then existing solutions usually have to settle with an "approximating"version as a substitute, leading to unexpected results for the underlying graph algorithm and thus reducing the practicality of a multi-version graph system for many application scenarios *** this paper, we observe that only a few graph updates have a great impact on the final results. We therefore present AFaVS, a novel multi-version graph system that can improve accuracy effectively in both time- and memory-efficient manners. The cornerstone of AFaVS lies in a novel concept "value"that characterizes the importance of graph updates. AFaVS proposes differential management of updates based on their values and achieves higher accuracy while preserving processing and memory efficiency. AFaVS is also equipped with value-guided version switching and locality-aware optimizations to boost its overall efficiency. Our results on a variety of real-world datasets show that AFaVS outperforms four state-of-the-art multi-version graph systems by 74.35%~95.72% in terms of accuracy improvement and 57.03%~90.44% in terms of memory reduction while introducing less than 2.96% extra computing time. We have deployed AFaVS in a disaster recovery system on the production cluster of Alibaba, achieving 78.8%~90.1% fewer error rates than advanced systems at a comparable efficiency. © 2023 IEEE.

关键词： Efficiency

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud computing Environment

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IEEE Transactions on intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of intelligent Transportation Systems. Vehicular Cloud computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient 41

Double Variance Reduction: A Smoothing Trick for Composite O...

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41st International Conference on Machine Learning, ICML 2024

作者： Di, Hao Ye, Haishan Zhang, Yueling Chang, Xiangyu Dai, Guang Tsang, Ivor W. Center for Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University China SGIT AI Lab State Grid Corporation of China China International Business School Beijing Foreign Studies University Beijing China Singapore College of Computing and Data Science NTU Singapore

Variance reduction techniques are designed to decrease the sampling variance, thereby accelerating convergence rates of first-order (FO) and zeroth-order (ZO) optimization methods. However, in composite optimization problems, ZO methods encounter an additional variance called the coordinate-wise variance, which stems from the random gradient estimation. To reduce this variance, prior works require estimating all partial derivatives, essentially approximating FO information. This approach demands O(d) function evaluations (d is the dimension size), which incurs substantial computational costs and is prohibitive in high-dimensional scenarios. This paper proposes the Zeroth-order Proximal Double Variance Reduction (ZPDVR) method, which utilizes the averaging trick to reduce both sampling and coordinate-wise variances. Compared to prior methods, ZPDVR relies solely on random gradient estimates, calls the stochastic zeroth-order oracle (SZO) in expectation O(1) times per iteration, and achieves the optimal O(d(n+κ) log(1/ϵ)) SZO query complexity in the strongly convex and smooth setting, where κ represents the condition number and ϵ is the desired accuracy. Empirical results validate ZPDVR's linear convergence and demonstrate its superior performance over other related methods. Copyright 2024 by the author(s)

关键词： Stochastic systems

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A GPU Algorithm for Solving the Positions of New Pulsars

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research in Astronomy and Astrophysics 2024年第11期24卷 46-52页

作者： Ziyao Fang Weiwei Zhu Chenchen Miao Yukai Zhou Dejiang Zhou Tianlu Chen Qiuyang Fu Lingqi Meng Xueli Miao Jiarui Niu Mengyao Xue The Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China School of Physics Peking UniversityBeijing 100871China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China

Timing newly discovered pulsars requires gradually building up a timing model that connects observations taken days to months *** sometimes can be challenging when our initial knowledge of the pulsar’s position is arcminutes off from its true *** a position error leads to significant arrival time shifts as a result of the Earth’s orbital *** down-hill fitting timing algorithms become ineffective when our model predicts the wrong pulse rotations for our next *** some pulsars whose model prediction is not too far off,the correct rotation number could be found by trial-and-error *** the remaining challenging pulsars,a more generalized method is called *** paper proposes a GPU-based algorithm that could exhaustively search a large area of trail positions for probable timing *** could help find phase-connected timing solutions for new pulsars using brute force.

关键词： methods data analysis-(stars:)pulsars general-(stars:)pulsars individual J0706+2707,J2149+5643

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Can Pretrained Language Models Reason on Sparse Commonsense Knowledge Graph? 8

Can Pretrained Language Models Reason on Sparse Commonsense ...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Hu, Yaxuan Xu, Weizhi Liu, Qiang Wang, Liping Wu, Shu School of Artificial Intelligence University of Chinese Academy of Sciences Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Science China

ISBN: (纸本)9781665450515

Commonsense knowledge is the knowledge shared by most humans, which is always stored in commonsense knowledge graph (CKG) as triplets. In this paper, we focus on the task of CKG competition, whose target is to predict the tail (head) target given the head (tail) entity and the relation. Most existing works employ the graph-based models, which aggregate information from neighboring entities on CKG. Despite their effectiveness, they still suffer from two main weaknesses. Firstly, the semantic relations between head and tail entities are neglected. Secondly, due to the sparsity of CKG, they rely on the graph densification that it will bring unexpected noises. To solve these problems, we propose a unified framework for COmmonSense knowledge graph completion based on BERT, namely COS-BERT. Firstly, we transfer each triplet into a natural sentence. Then, we fine-tune the pretrained language model using the transformed sentences. Finally, we rank the candidates based on the output representation of sentences. Furthermore, we add a pre-filter to obtain a subset of candidates on the inference stage to save unnecessary computation costs. Comprehensive experiments have demonstrated the superiority of COS-BERT over the state-of-the-arts. © 2022 IEEE.

关键词： Knowledge graph

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Observer-Based Control for a New Stochastic Maximum Power Point Tracking for Photovoltaic Systems With Networked Control System

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IEEE Transactions on Fuzzy Systems 2023年第6期31卷 1870-1884页

作者： Aslam, Muhammad Shamrooz Tiwari, Prayag Pandey, Hari Mohan Band, Shahab S. Guangxi University of Science and Technology School of Automation Liuzhou545006 China Halmstad University School of Information Technology Halmstad301 18 Sweden Bournemouth University Data Science and Artificial Intelligence Department of Information and Computing PooleBH12 United Kingdom National Yunlin University of Science and Technology Future Technology Research Center College of Future Douliou64002 Taiwan

This study discusses the new stochastic maximum power point tracking control approach toward the photovoltaic cells (PCs). A PC generator is isolated from the grid, resulting in a direct current microgrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposed networked control systems beneath an event-triggered approach basically in the fuzzy system. In this scenario, we look at how random, variable loads impact the PC generator's stability and efficiency. The basic premise of this article is to load changes and the value matching to a Markov chain. PC generators are complicated nonlinear systems that pose a modeling problem. Transforming this nonlinear PC generator model into the Takagi-Sugeno (T - S) fuzzy model is another option. The T - S fuzzy model is presented in a unified framework, for which 1) the fuzzy observer based on this premise variables can be used for approximately in the infinite states to the present system, 2) the fuzzy observer-based controller can be created using the same premises being the observer, and 3) to reduce the impact of transmission burden, an event-triggered method can be investigated. Simulation in the PC generator model for the real-time climate data obtained in China demonstrates the importance of our method. In addition, by using a new Lyapunov-Krasovskii functional for combining with the allowed weighting matrices incorporating mode-dependent integral terms, the developed model can be stochastically stable and achieves the required performances. Based on the tensor-product (T-P) transformation, a new depiction of the nonlinear system is derived in two separate steps in which an adequate controller input is guaranteed in the first step and an adequate vertex polytope is ensured in the second step. To present the potential of our proposed method, we simulate it for PC generators. © 1993-2012 IEEE.

关键词： Linear matrix inequalities

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Novel signal-consistency test for gravitational-wave searches of generic black hole binaries

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Physical Review D 2024年第2期110卷 023042-023042页

作者： Stefano Schmidt Sarah Caudill Science Park 105 1098 XG Amsterdam The Netherlands Institute for Gravitational and Subatomic Physics (GRASP) Department of Physics Center for Scientific Computing and Data Science Research

We propose a novel signal-consistency test applicable to a broad search for gravitational waves emitted by generic binary black hole (BBH) systems. The test generalizes the time domain ξ2 signal-consistency test currently utilized by the gstlal pipeline, which quantifies the discrepancy between the expected signal-to-noise ratio time series with the measured one. While the traditional test is restricted to aligned-spin circular orbits and does not account for higher-order modes (HMs), our test does not make any assumption on the nature of the signal. After addressing the mathematical details of the new test, we quantify its advantages in the context of searching for precessing BBHs and/or BBHs with HM content. Our results reveal that, for precessing signals, the new test is optimal and has the potential to reduce the values of the ξ2 statistics by up to 2 orders of magnitude when compared to the standard test. However, in the case of signals with HM content, only a modest enhancement is observed. Recognizing the computational burden associated with the new test, we also derive an approximated signal-consistency test. This approximation maintains the same computational cost as the standard test and can be easily implemented in any matched-filtering pipeline with minimal changes, sacrificing only a few percent of accuracy in the low-SNR regime. However, in the high-SNR regime, the approximated signal-consistency test does not bring any improvement as compared to the “standard” one. By introducing our new test and its approximation and understanding their validity and limitation, this work will benefit any matched-filtering pipeline aimed at searching for BBH signals with strong precession and/or HM content.

关键词： Gravitational waves data analysis

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FedEdge: Accelerating Edge-Assisted Federated Learning 23

FedEdge: Accelerating Edge-Assisted Federated Learning

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients' local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds;2) it does not recognize or utilize model diversity when producing a global model;and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × -3.20 ×, and improves model accuracy by 2.14% - 6.63%. © 2023 ACM.

关键词： Edge computing

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4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1485-1499页

作者： Gang Wang Weisheng Li Mingliang Zhou Haobo Zhu Guang Yang Choon Hwai Yap Chongqing Key Laborotory of Image Rocognition Chongqing University of Posts and TelecommunicationsChongqingChina School of Computing and Data Engineering NingboTech UniversityNingboChina Department of Bioengineering Imperial College LondonLondonUK Key Laboratory of Cyberspace Big Data Intelligent Security(Chongqing University of Posts and Telecommunications) Ministry of EducationChongqingChina School of Computer Science Chongqing UniversityChongqingChina University of Oxford OxfordUK Cardiovascular Research Centre Royal Brompton HospitalLondonUK National Heart and Lung Institute Imperial College LondonLondonUK

Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images,making it difficult to rapidly and easily examine for this ***,the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS(RDeH),which we have named ***,the framework implements a coarseto-fine two-stage detection approach,with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains,and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician *** experiments extensively compare the authors’network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits:(1)it achieved superior average accuracy of 99.37%on three categories of foetal ultrasound images from different cases;(2)it demonstrates visually fine detection performance with weakly supervised *** framework could be used to accelerate the diagnosis of eHLHS,and hence significantly lessen reliance on experienced medical physicians.

关键词： 4D deep learning fetal cardiac ultrasound hypoplastic left heart syndrome weakly-supervised learning

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