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Trustworthy Sensor Fusion Against Inaudible Command Attacks in Advanced Driver-Assistance Systems

IEEE Internet of Things Journal 2023年第19期10卷 17254-17264页

作者： Guan, Jiwei Pan, Lei Wang, Chen Yu, Shui Gao, Longxiang Zheng, Xi Macquarie University School of Computing SydneyNSW2109 Australia CSIRO Data61 SydneyNSW1710 Australia Deakin University School of Information Technology Waurn PondsVIC3125 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Qilu University of Technology Shangdong Computer Science Center Jinan250316 China

There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-based multimodal fusion for defense, without considering the model uncertainty in trustworthiness. As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios. In this article, we propose the multimodal fusion framework (MFF) as an intelligent security system to defend against inaudible voice command attacks. MFF fuses heterogeneous audio-vision modalities using VGG family neural networks and achieves the detection accuracy of 92.25% in the comparative fusion method empirical study. Additionally, extensive experiments on audio-vision tasks reveal the model's uncertainty. Using expected calibration errors, we measure calibration errors and Monte Carlo Dropout to estimate the predictive distribution for the proposed models. Our findings show empirically to train robust multimodal models, improve standard accuracy and provide a further step toward interpretability. Finally, we discuss the pros and cons of our approach and its applicability for advanced driver assistance systems. © 2014 IEEE.

关键词： Speech recognition

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Efficient Fourier Filtering Network with Contrastive Learning for UAV-based Unaligned Bi-modal Salient Object Detection

arXiv

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arXiv 2024年

作者： Lyu, Pengfei Yeung, Pak-Hei Cheng, Xiufei Yu, Xiaosheng Wu, Chengdong Rajapakse, Jagath C. The Faculty of Robot Science and Engineering Northeastern University Shenyang110169 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore

Unmanned aerial vehicle (UAV)-based bi-modal salient object detection (BSOD) aims to segment salient objects in a scene utilizing complementary cues in unaligned RGB and thermal image pairs. However, the high computational expense of existing UAV-based BSOD models limits their applicability to real-world UAV devices. To address this problem, we propose an efficient Fourier filter network with contrastive learning that achieves both real-time and accurate performance. Specifically, we first design a semantic contrastive alignment loss to align the two modalities at the semantic level, which facilitates mutual refinement in a parameter-free way. Second, inspired by the fast Fourier transform that obtains global relevance in linear complexity, we propose synchronized alignment fusion, which aligns and fuses bi-modal features in the channel and spatial dimensions by a hierarchical filtering mechanism. Our proposed model, AlignSal, reduces the number of parameters by 70.0%, decreases the floating point operations by 49.4%, and increases the inference speed by 152.5% compared to the cutting-edge BSOD model (i.e., MROS). Extensive experiments on the UAV RGB-T 2400 and three weakly aligned datasets demonstrate that AlignSal achieves both real-time inference speed and better performance and generalizability compared to sixteen state-of-the-art BSOD models across most evaluation metrics. In addition, our ablation studies further verify AlignSal's potential in boosting the performance of existing aligned BSOD models on UAV-based unaligned data. The code is available at: https://***/JoshuaLPF/AlignSal. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Dynamical analysis,control,boundedness,and prediction for a fractional-order financial risk system

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Chinese Physics B 2024年第11期33卷 223-235页

作者： Kehao Yang Song Zheng Tianhu Yu Aceng Sambas Muhamad Deni Johansyah Hassan Saberi-Nik Mohamad Afendee Mohamed School of Data Science Zhejiang University of Finance & EconomicsHangzhou 310018China Department of Mathematics Luoyang Normal UniversityLuoyang 471934China Faculty of Informatics and Computing Universiti Sultan Zainal AbidinBesut Campus22200TerengganuMalaysia Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaTasikmalayaJawa Barat46196Indonesia Department of Mathematics Universitas PadjajdaranJatinangorKabupaten Sumedang 45363Indonesia Department of Mathematics and Statistics University of NeyshaburNeyshabur ***Iran

This paper delves into the dynamical analysis,chaos control,Mittag–Leffler boundedness(MLB),and forecasting a fractional-order financial risk(FOFR)system through an absolute function *** this end,the FOFR system is first proposed,and the adomian decomposition method(ADM)is employed to resolve this fractional-order *** stability of equilibrium points and the corresponding control schemes are assessed,and several classical tools such as Lyapunov exponents(LE),bifurcation diagrams,complexity analysis(CA),and 0–1 test are further extended to analyze the dynamical behaviors of *** the global Mittag–Leffler attractive set(MLAS)and Mittag–Leffler positive invariant set(MLPIS)for the proposed financial risk(FR)system are ***,a proficient reservoir-computing(RC)method is applied to forecast the temporal evolution of the complex dynamics for the proposed system,and some simulations are carried out to show the effectiveness and feasibility of the present scheme.

关键词： FOFR system dynamical analysis control boundedness forecasting

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The Frontier of data Erasure: Machine Unlearning for Large Language Models

arXiv

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arXiv 2024年

作者： Qu, Youyang Ding, Ming Sun, Nan Kanchana Zhu, Tianqing Niyato, Dusit Data61 Commonwealth Scientific and Industrial Research Organization Australia School of Systems & Computing University of New South Wales Australia School of Computer Science University of Sydney Australia Faculty of Data Science City University of Macau China College of Computing & Data Science Nanyang Technological University Singapore

Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation. Nonetheless, they pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information from their vast datasets. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns, offering techniques for LLMs to selectively discard certain data. This paper reviews the latest in machine unlearning for LLMs, introducing methods for the targeted forgetting of information to address privacy, ethical, and legal challenges without necessitating full model retraining. It divides existing research into unlearning from unstructured/textual data and structured/classification data, showcasing the effectiveness of these approaches in removing specific data while maintaining model efficacy. Highlighting the practicality of machine unlearning, this analysis also points out the hurdles in preserving model integrity, avoiding excessive or insufficient data removal, and ensuring consistent outputs, underlining the role of machine unlearning in advancing responsible, ethical AI. © 2024, CC BY.

关键词： Computational linguistics

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang 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 Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China 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

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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Parallel Image Encryption Algorithm Using Partitioned Cellular Automata on Graphic Processor Unit

Parallel Image Encryption Algorithm Using Partitioned Cellul...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Fazlali, Mahmood Alihoseini, Amirhossein Zarei, Ebrahim Tabani, Myasar Pourmoafi, Seyedali University of Hertfordshire Cybersecurity and Computing Systems Research Group Hatfield United Kingdom Shahid Beheshti University Faculty of Mathematical Sciences Dept. of Data and Computer Sciensce Tehran Iran Khansar Campus University of Isfahan Dep. of Computer Science Isfahan Iran

ISBN: (纸本)9798350346473

Image encryption is a reliable method for securely transmitting images over a network. The time required to encrypt and decrypt an image in online applications is also very important. Although cellular automata cryptography is an appropriate technique for parallelizing and accelerating cryptographic methods, its capacity cannot be demonstrated only in multi-core platforms. Thus, it is needed to parallelize cellular automata cryptography on Graphic Processor Units (GPUs) in order to significantly decrease the encryption/decryption time. In this paper, we propose a new parallel algorithm for two-dimensional cellular automata cryptography that is implemented on GPU. The proposed algorithm uses multiple threads at once to accelerate the bit-level permutation and substitution operations by taking into account the capacity of cellular automata in parallel processing. According to the study experimental findings, the proposed algorithm performs faster on GPU compared to a multicore platform while maintaining the same level of security in comparison to the serial algorithm. © 2023 IEEE.

关键词： Graphics processing unit

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Enhancing Wound Care in Healthcare Systems with Logistic Regression for Infection Detection 1

Enhancing Wound Care in Healthcare Systems with Logistic Reg...

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1st International Conference on Innovations in Communications, Electrical and Computer Engineering, ICICEC 2024

作者： Ramalingam, L. Amarnath, Raveendra N. Arun, V. Sendhilkumar, N.C. Ravi, Rupavathy Srinivasan, C. Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Mphasis Corporation MelissaTX United States SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Agni College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

In healthcare systems, wound care management is essential for reducing infections and promoting faster recovery. To improve wound care, this paper proposes a data-driven method that combines machine learning (ML), more particularly, Logistic Regression (LR), with Internet of Things (IoT) sensors to identify early infections. The system provides real-time data streams to healthcare providers by continually monitoring important wound parameters, including temperature, moisture, pH levels, and exudate presence via IoT-enabled sensors. Using LR, the system predicts the potential of wound infections based on these physiological indicators, allowing for more effective patient care and early treatments. When tested for accuracy and dependability in infection prediction, the proposed model shows significant detection skills with few false positives. This approach optimizes clinical processes by reducing the need for manual wound examination, enhancing patient safety, and lowering the possibility of complications from infections. Wound care management might improve patient outcomes and expedite infection control measures in medical institutions. © 2024 IEEE.

关键词： Logistic regression

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Advanced Arabic Alphabet Sign Language Recognition Using Transfer Learning and Transformer Models

Advanced Arabic Alphabet Sign Language Recognition Using Tra...

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International Conference on Computer and Applications (ICCA)

作者： Mazen Balat Rewaa Awaad Hend Adel Ahmed B. Zaky Salah A. Aly CS & IT Dept. Egypt-Japanese University of Science & Technology Alexandria Egypt Computer Science Faculty of Science Fayoum University Fayoum Egypt Faculty of Computing & Data Science Badya University Giza Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This paper presents an Arabic Alphabet Sign Language recognition approach, using deep learning methods in conjunction with transfer learning and transformer-based models. We study the performance of the different variants on two publicly available datasets, namely ArSL2018 and AASL. This task will make full use of state-of-the-art CNN architectures like ResNet50, MobileNetV2, and EfficientNetB7, and the latest transformer models such as Google ViT and Microsoft Swin Transformer. These pre-trained models have been fine-tuned on the above datasets in an attempt to capture some unique features of Arabic sign language motions. Experimental results present evidence that the suggested methodology can receive a high recognition accuracy, by up to 99.6% and 99.43% on ArSL2018 and AASL, respectively. That is far beyond the previously reported state-of-the-art approaches. This performance opens up even more avenues for communication that may be more accessible to Arabic-speaking deaf and hard-of-hearing, and thus encourages an inclusive society.

关键词： Sign language Accuracy Translation Computational modeling Transfer learning Computer architecture Transformers Robustness Real-time systems Residual neural networks

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Detection of construction worker safety protection equipment based on fast YOLO

Detection of construction worker safety protection equipment...

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2024 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Li, Xin Hussin, Norriza Ismail, Nor Alina Faculty of Engineering Built Environment & Information Technology SEGi University Selangor Darul Ehsan 547810 Malaysia Faculty of Data Science and Computing University Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Research and Development Hebei Voin Environmental Protection Technology Co. Ltd. Hebei054000 China

ISBN: (纸本)9781510682597

Enterprises are often concerned about disasters, particularly accidents involving personnel. Therefore, there is ongoing research dedicated to safeguarding the safety of employees within enterprises. In recent years, with the continuous development of machine vision technology, industry and academia have been working to adopt machine vision methods to address safety hazards in workers' production. Machine vision applications are still being researched for specific sectors and lack generalization. Algorithms commonly face issues of high computational complexity and demanding hardware requirements. This paper adopts the lightweight YOLOV5 as the baseline algorithm and enhances its accuracy using a receptive field attention mechanism. SeNet is introduced to improve the generalization of object detection, and IDetect Head is employed to increase the efficiency of the detection head. Ultimately, the algorithm's accuracy is enhanced by 3.7%, and mAP50 is increased by 3.0%. This algorithm can be deployed to Internet of Things (IoT) machine vision terminals, reducing deployment costs and improving monitoring efficiency. © 2024 SPIE.

关键词： Construction equipment

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Educational video classification by using a transcript to image transform and supervised learning

Educational video classification by using a transcript to im...

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7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017

作者： Chatbri, Houssem Oliveira, Marlon McGuinness, Kevin Little, Suzanne Kameyama, Keisuke Kwan, Paul Sutherland, Alistair O'Connor, Noel E. Insight Centre for Data Analytics Dublin City University Ireland School of Computing Dublin City University Ireland Faculty of Engineering Information and Systems University of Tsukuba Japan School of Science and Technology University of New England NSW Australia

ISBN: (纸本)9781538618417

In this work, we present a method for automatic topic classification of educational videos using a speech transcript transform. Our method works as follows: First, speech recognition is used to generate video transcripts. Then, the transcripts are converted into images using a statistical cooccurrence transformation that we designed. Finally, a classifier is used to produce video category labels for a transcript image input. For our classifiers, we report results using a convolutional neural network (CNN) and a principal component analysis (PCA) model. In order to evaluate our method, we used the Khan Academy on a Stick dataset that contains 2,545 videos, where each video is labeled with one or two of 13 categories. Experiments show that our method is effective and strongly competitive against other supervised learning-based methods. © 2017 IEEE.

关键词： Principal component analysis

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