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Detection and Mitigation of GPS Attack via Cooperative Localization

Detection and Mitigation of GPS Attack via Cooperative Local...

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IEEE international conference on Industrial Informatics (INDIN)

作者： Zhuang Wang Zhenpo Wang Jianhong Liu Guoqiang Li Beijing Bitnei Corp. Ltd School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Mechanical Engineering Beijing Institute of Technology Beijing China Intelligent Hardware R&D Beijing Bitnei Corp. Ltd Beijing China

Connected automated vehicles (CAVs) share information through vehicular networks; however, cyber-attacks on GPS may cause significant challenges to compromise vehicle security and driving safety. In this paper, a novel approach for GPS attack detection and mitigation is proposed using vehicle-to-vehicle (V2V) communication, which enables vehicles to access and utilize accurate location information for autonomous driving. Instead of directly fusing the location data received from other vehicles, a trust evaluation process with a $\chi$2-detector is developed to identify and isolate potential malicious surrounding vehicles that may send erroneous information into the V2V network. Subsequently, a Bayesian approach is employed to fuse data from GPS, inter-vehicle distance, and bearing angle measurements. A real-time Robust-Random-Cut-Forest based detector is constructed to identify possible GPS attacks for an ego vehicle. When a malicious attack is detected, a novel cooperative positioning method is used to mitigate the impact of the GPS attack based on V2V information. Simulation results demonstrate the performance of the proposed approach in detecting GPS attacks timely and improving the positioning accuracy and robustness of CAVs under different attacks.

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SCAF: Skip-Connections in Auto-encoder for Face Alignment with Few Annotated data 21st

SCAF: Skip-Connections in Auto-encoder for Face Alignment wi...

引用

21st international conference on Image Analysis and Processing (ICIAP)

作者： Dornier, Martin Gosselin, Philippe-Henri Raymond, Christian Ricquebourg, Yann Couasnon, Bertrand InterDigital Cesson Sevigne France Univ Rennes CNRS IRISA Rennes France

ISBN: (纸本)9783031064272;9783031064265

Supervised face alignment methods need large amounts of training data to achieve good performance in terms of accuracy and generalization. However face alignment datasets rarely exceed a few thousand samples making these methods prone to overfitting on the specific training dataset. Semi-supervised methods like TS3 or 3FabRec have emerged to alleviate this issue by using labeled and unlabeled data during the training. In this paper we propose Skip-Connections in Auto-encoder for Face alignment (SCAF), we build on 3FabRec by adding skip-connections between the encoder and the decoder. These skip-connections lead to better landmark predictions, especially on challenging examples. We also apply for the first time active learning to the face alignment task and introduce a new acquisition function, the Negative Neighborhood Magnitude, specially designed to assess the quality of heatmaps. These two proposals show their effectiveness on several face alignment datasets when training with limited data.

关键词： Face alignment Semi-supervised training Active learning

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An Im2col Architecture Using The Benes Network For Deep learning Hardware Accelerators

An Im2col Architecture Using The Benes Network For Deep Lear...

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international SoC Design conference, ISOCC

作者： Sanghyun Kim Kyungho Kim Yumi Kim Aeri Kim Sung-Joon Jang Intelligent Image Processing Research Center Korea Electronics Technology Institute South Korea

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

General matrix-matrix multiply (GEMM) has a significant effect on the performance of deep learning hardware accelerators. Image to column (im2col) is a process that rearranges input data images to facilitate efficient GEMM operation. This paper proposes a method for implementing the im2col process using a benes network. The proposed method eliminates bottlenecks occurring in the im2col process and achieves a 33% reduction in on-chip memory usage. Additionally, depending on the routing algorithm of the benes network, images can be reorganized into the desired configuration.

关键词： Deep learning Routing System-on-chip Hardware acceleration

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Summary of Semantic Segmentation in Small Samples

Summary of Semantic Segmentation in Small Samples

引用

international conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Zhang Yixin Chen Wenyu Zeng Yuting School of Computer Science and Engineering (School of Cybersecurity) University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Small-sample semantic segmentation is an important task in computer vision. Its purpose is to achieve accurate pixel-level classification in the case of very limited annotation data. Small sample scenarios usually do not have enough annotated data to train deep learning models, so the models are easy to overfit and have insufficient generalization performance. It mainly includes techniques based on meta-learning, generative models and transfer learning. Meta-learning improves the efficiency of the model by learning how to quickly adapt to the new task, and the generative model expands the data set by generating synthetic data, while transfer learning helps improve the segmentation performance by extracting knowledge from tasks in related fields.

关键词： Metalearning Adaptation models Computer vision Annotations Reviews Computational modeling Semantic segmentation Transfer learning data models Synthetic data

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Povm-Based Quantum Self-Attention Neural Network 21

Povm-Based Quantum Self-Attention Neural Network

引用

21st international conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2023

作者： Wei, Jiachun He, Zhimin Chen, Chuangtao Deng, Maijie Situ, Haozhen School of Mathematics and Big data Foshan University Foshan528000 China School of Electronic and Information Engineering Foshan University Foshan528000 China Macau University of Science and Technology Faculty of Innovation Engineering Macau China School of Mechatronic Engineering and Automation Foshan University Foshan528000 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

ISBN: (纸本)9798350303810

Quantum Self-Attention Neural Network (QSANN) has demonstrated remarkable potential. However, it is limited to measuring only partial information from qubits, thereby restricting its information extraction capability. Moreover, the measurement process requires multiple measurements for each qubit, leading to inefficiency in utilizing the feature space. To address these challenges, this paper introduces a novel approach called Positive Operator-Valued Measure based Quantum Self-Attention Neural Network (POVM-QSANN). It leverages POVM operators as observables, enabling the extraction of comprehensive QKV (Query-Key-Value) feature vectors from each qubit. This innovative mechanism significantly enhances information extraction capability and efficiently utilizes the feature space. The experimental results underscore the considerable advancements of POVM-QSANN on MC and RP datasets. Particularly noteworthy is its remarkable accuracy increase of 9.68% compared to QSANN on the RP dataset. © 2023 IEEE.

关键词： Positive Operator-Valued Measure Quantum machine learning Self-attention mechanism Variational quantum algorithm

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CPNet: Controllable Point Cloud Generation Network Using Part-Level Information 21st

CPNet: Controllable Point Cloud Generation Network Using Par...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Qin, Shun Han, WenZhuo Zhang, Jinlai Yang, Wenqi Gao, Kai Li, Jin College of Computer and Communication Engineering Changsha University of Science and Technology Changsha China College of Automotive and Mechanical Engineering Changsha University of Science and Technology Changsha China Microelectronics and SolidState Electronics Nanjing Institute of Electronic Devices Nanjing China

ISBN: (纸本)9789819601219

The generation of 3D point clouds with controllable part-level details remains a challenging task in the field of computer graphics and vision. Existing methods often produce monolithic point clouds, lacking the flexibility for part editing and transformation necessary for various applications such as object modeling, animation, and scene reconstruction. To address this limitation, we introduce the Controllable Point Cloud Generation Network (CPNet), a novel probabilistic generative model tailored for part-level point cloud generation. Our approach consists of three main components: a Segment Paerts (SP) module that establishes the foundational learning for parts, a Part selector and Transformation sampler that models the distribution of points probabilistically, and a Cross-Attention Diffusion (CAD) module with special attention mechanism designed to denoise and refine the generated point cloud. By decomposing the point cloud at the part-level, CPNet enables the generation of point clouds that can be controlled through part-level editing and replacement. The proposed method achieves state-of-the-art (SOTA) results in terms of point cloud quality and offers unparalleled control over the generation process. We demonstrate the effectiveness of CPNet through extensive experiments and comparisons with existing methods, highlighting its potential for applications in computer graphics, robotics, and other domains requiring high-quality, controllable 3D point cloud data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation 41

Hyperbolic Geometric Latent Diffusion Model for Graph Genera...

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41st international conference on Machine learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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NimbleLearn: A Scalable and Fast Batch-mode Active learning Approach 21

NimbleLearn: A Scalable and Fast Batch-mode Active Learning ...

引用

21st IEEE international conference on data Mining (IEEE ICDM)

作者： Kong, Ruoyan Qiu, Zhanlong Liu, Yang Zhao, Qi Univ Minnesota Minneapolis MN 55455 USA Amazon Seattle WA USA

ISBN: (纸本)9781665424271

Batch-mode active learning iteratively selects a batch of unlabeled samples for labelling to maximize model performance and reduce total runtime. To select the most informative and diverse batch, existing methods usually calculate the correlation between samples within a batch, leading to combinatorial optimization problems which are inefficient, complex, and limited to linear models for approximated solutions. In this paper, we propose NimbleLearn, a scalable deep imitation batch-mode active learning approach to address these drawbacks. NimbleLearn sequentially predicts an "ideal sample" by a deep policy network for each batch. Such ideal sample maximizes the model performance when combined with the labeled samples and the already-selected samples in the current batch. Unlike the existing batch-mode active learning methods which directly select one batch of samples from unlabeled ones, NimbleLearn reduces the dimension of the policy network output to the number of features (assuming the number of unlabeled samples is much greater than the number of features). In addition, NimbleLearn is a general framework and can be applied in both linear and nonlinear models. Experiments conducted on 4 public datasets show NimbleLearn can achieve similar or better performance as existing SOTA algorithms, while reducing the number of labeled samples and runtime by over 50%.

关键词： active learning batch-mode imitation learning

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Enhanced ELMAN Neural Network learning Using Sine Cosine Optimization 21

Enhanced ELMAN Neural Network Learning Using Sine Cosine Opt...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Iqtidar, Ali Jianping, Li Pervez, Fatima Narejo, Attaullah Luo, Jiaheng Yeasin, Arafat Saboor, Abdus Yulu, Liu School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan611 731 China Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan

ISBN: (纸本)9798331519254

This paper explores an enhanced learning model of the Elman Neural Network with a view of addressing problems of local minima and slow convergence time by using the sine cosine algorithm. Elman Neural Network that is a sort of Artificial Neural Networks is also widely applied to most classification tasks due to its simplicity and effectiveness. However, it faces challenges like;it is slower in learning and when performing an optimization process it gets trapped in local Optima. The research presents a SCA-ElmanNN model which incorporates of exploration and exploitation aspect of sine cosine algorithm to improve Elman Neural Network performance. Using Thyroid dataset, the study examines the effectiveness of the proposed hybrid model with other models for the comparison purpose, including SCA-BPNN and SCA-ANN. The results clarify that upgraded SCA-ElmanNN model enhances the accuracy criteria and minimizes mean square error in both training and testing sets. The training set of Thyroid dataset had an accuracy of 99.9587% and mean square error of 0.000473411 with SCA-ElmanNN, outperforming all the assessed models. Thus, some amount of enhancement has been visible in the Thyroid dataset. This study shows that an integration of SCA with ElmanNN results to improved classification performance and highlights the applicability of meta-heuristic algorithms like Sine Cosine in handling of optimization problems within neural networks. The findings inform the future research on the integration of intelligent optimization algorithms for training artificial neural networks. © 2024 IEEE.

关键词： Elman neural networks

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Generate while Sensing - intelligent Imaging with Memristive Pixel-CNN 21

Generate while Sensing - Intelligent Imaging with Memristive...

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21st IEEE international conference on Nanotechnology (IEEE NANO)

作者： Bakambekova, A. Krestinskaya, O. James, A. P. King Abdullah Univ Sci & Technol Thuwal Saudi Arabia IIITM K Digital Univ Kazhakkoottam Kerala India

ISBN: (纸本)9781665441568

Gated Pixel Convolution Neural Network (PixelCNN) is a computationally intensive network that is useful for generating visual data. The prediction and generating pixels is a challenging but useful task for many fields such as forensics, machine vision and robotics. However, implementing PixelCNN in edge devices is a challenging task due to learning complexity and computational limits. In this paper, we present the design of neuro-memristive circuits for computing PixelCNN cells in analog domain as a co-processor unit in edge devices. The architecture was designed using 180nm CMOS technology and carbon-chalcogenide memristive devices. On-chip area of the proposed architecture unit is 24.756mm(2), while power depends on the size of the input image and the configuration of the overall network. The power required to generate the images sequentially is 154.336mW.

关键词： Training Visualization Image edge detection Memristors Computer architecture Logic gates Robot sensing systems

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