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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Liu, Dongyu Wang, Xuhong Chen, Cen Wang, Yanhao Yao, Shengyue Lin, Yilun School of Data Science and Engineering East China Normal University Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798331505929

In recent years, there has been an increasing interest in image anonymization, particularly focusing on the de-identification of faces and individuals. However, for self-driving applications, merely de-identifying faces and individuals might not provide sufficient privacy protection since street views like vehicles and buildings can still disclose locations, trajectories, and other sensitive information. Therefore, it remains crucial to extend anonymization techniques to street view images to fully preserve the privacy of users, pedestrians, and vehicles. In this paper, we propose a Street View Image Anonymization (SVIA) framework for self-driving applications. The SVIA framework consists of three integral components: a semantic segmenter to segment an input image into functional regions, an inpainter to generate alternatives to privacy-sensitive regions, and a harmonizer to seamlessly stitch modified regions to guarantee visual coherence. Compared to existing methods, SVIA achieves a much better trade-off between image generation quality and privacy protection, as evidenced by experimental results for five common metrics on two widely used public datasets. © 2024 IEEE.

关键词： Anonymity

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Image co-segmentation based on pyramid features cross-correlation network

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Science China(Information Sciences) 2023年第1期66卷 316-317页

作者： Jia CHEN Yasong CHEN Weihao LI Zhi LIU Sannyuya LIU Zongkai YANG Faculty of Artificial Intelligence in Education Central China Normal University Data61 Commonwealth Scientific and Industrial Research Organisation (CSIRO) National Engineering Laboratory for Educational Big Data Central China Normal University

Dear editor,Co-segmentation aims to segment objects with the same semantic information that simultaneously appears in two or multiple images. Vicente et al. [1] proposed the definition of object co-segmentation, i.e., “The task of jointly segmenting‘something similar' in a group or a pair of images is commonly referred to as co-segmentation”.

关键词： Semantics

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Efficient Matrix-Based Multi-view Projection Features Combined for Multi-modal 3D Semantic Segmentation 21st

Efficient Matrix-Based Multi-view Projection Features Combin...

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21st Pacific Rim International Conference on artificial intelligence, PRICAI 2024

作者： Zhao, Jiayu Wei, Fei-Fei Liang, Anqi Mei, Kuizhi College of Artificial Intelligence Xi’an Jiaotong University Xi’an China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center of Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9789819601219

This paper introduces BFVTModel, an efficient 3D semantic segmentation Model. Multi-modal segmentators, which employ LiDAR and Camera sensors as input, have gained popularity owing to their capacity to leverage the semantic information in image data and complementary details in point cloud data. However, the segmentation model using multi-modal input faces some problems, such as the modal consistency problem and the model complexity problem. In this paper, we propose utilizing the idea of an Occupancy-based multi-view feature combination method to obtain the 3D information of the features of the two modalities independently. We also design a projection structure to obtain the three-axis features in different modalities according to the information, including the main view, side view, and top view features. We construct a module called Feature View Transform (FVT) that combines three-axis plane features with bias. The CNN neural network is used to replace attention mechanisms, reducing the dimensionality of the original three-dimensional space matrix, thereby reducing the parameter count and increasing the model’s speed. We used a bilinear mapping structure to fuse LiDAR and Camera features and complete the 3D semantic segmentation task. We validate the model on the NuScenes dataset and obtain competitive accuracy results while leading the way in efficiency evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantic Segmentation

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Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 58-73页

作者： Qingsong Zhao Lei Shu Kailiang Li Mohamed Amine Ferrag Ximeng Liu Yanbin Li the College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031 the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023 the State Key Laboratory of Cryptology Beijing 100878China IEEE the School of Engineering University of LincolnLincoln LN67TSUK the Artificial Intelligence and Digital Science Research Center Technology Innovation InstituteMasdar City 9639UAE the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security *** issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

关键词： Challenges Internet of Things(IoT) privacy and security security requirements solar insecticidal lamps(SIL)

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A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

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Computers, Materials & Continua 2025年第1期82卷 997-1026页

作者： Li Ma Cai Dai Xingsi Xue Cheng Peng School of Computer Science Shaanxi Normal UniversityXi’an710119China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Information Construction and Management Center and Institute of Artificial Intelligence and Educational New Productivity Ningxia Normal UniversityGuyuan756099China

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization *** the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search ***,two update strategies based on decomposition are used to update the evolving population and external archive,***,a multiselection strategy is *** first strategy is for the subspace without a non-dominated *** the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global *** second strategy is for the subspace with a non-dominated *** the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local *** third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated *** the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to ***,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search ***,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test *** results show that the proposed algorithm has better performance.

关键词： Multi-objective optimization multi-objective particle swarm optimization decomposition multi-selection strategy

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SparallaxMPC: High-Speed Model Predictive Control Solver with High Sparsity and Parallelism

SparallaxMPC: High-Speed Model Predictive Control Solver wit...

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2024 China Automation Congress, CAC 2024

作者： Lei, ZhengXing Deng, Xiaodong Liu, Longjun Jiang, Caigui National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

ISBN: (纸本)9798350368604

Model Predictive Control (MPC) is one of the most popular methods used in control problem. However, MPC is computationally demanding with limited use of sparse and parallel implementations. We present SparallaxMPC, a high-speed MPC solver with parallel and sparse representation. Our approach is based on sparse matrix representation and leverages the structure of the MPC problem for efficiency. We demonstrate the effectiveness of SparallaxMPC by benchmarking it against the state-of-the-art solver CasADi, achieving more than a 3x acceleration. © 2024 IEEE.

关键词： Predictive control systems

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InstKD: Towards Lightweight 3D Object Detection With Instance-Aware Knowledge Distillation

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Zhang, Haonan Liu, Longjun Huang, Yuqi Lei, Xinyu Tong, Lei Wen, Bihan National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Deep neural network (DNN) is extensively explored for LiDAR-based 3D object detection, a crucial perception task in the field of autonomous driving. However, the presence of redundant parameters and complex computations pose challenges for the practical deployment of DNNs. Despite knowledge distillation (KD) is an effective approach for accelerating models, extremely small number of efforts explore its potential on LiDARbased 3D detectors. Besides, existing studies neglect to elaborately investigate 3D voxel-wise features for compression. To this end, we propose instance-aware knowledge distillation (InstKD) for 3D detector compression. The proposed method conducts KD by fully excavating two types of knowledge related to 3D voxelwise features. Firstly, the 3D voxel-wise feature of teacher is transferred to teach the student. In order to prioritize the knowledge with strong guiding capacity, we introduce expanded bounding box (E-Bbox) to distinguish and balance the foreground and background regions. Besides, we generate contribution map (CM) by calculating the gap between the classification response of teacher and student models to further dynamically balance individual instance for distillation. Secondly, we also align the relation-based knowledge of 3D voxel-wise features between the distillation pairs. To avoid incalculable relation on a massive number of 3D voxel-wise features, we distill the relation among instances selected by E-Bboxes, where the intra-relation of homogeneous instances and inter-relation of heterogeneous instances are transferred in a dual-pathway manner. In the experiments, we compress different models on benchmarks with varying scales. The results demonstrate that our method achieves the lightweight 3D detector with slight performance drop. For example, on KITTI dataset, our 2× compressed SECOND (75.5% parameters and 74.5% FLOPs reduction) achieves 66.83% mAP, surpassing its teacher model. The key code is available at https://***/zhnxj

关键词： Object detection

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Semi-Supervised Curriculum Learning for Ultra-Wide-Angle fundus Optic Disc Segmentation

Semi-Supervised Curriculum Learning for Ultra-Wide-Angle fun...

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2024 China Automation Congress, CAC 2024

作者： Wang, Yingkai Chen, Yiyi Wu, Jiayi Xin, Jingmin National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9798350368604

Segmentation of the optic disc in ultra-wide-angle fundus images could aid in detection and diagnosis of diabetic kidney disease and diabetic retinopathy. Due to the wide field of view, large image size, and small targets of ultra-wide-angle fundus images, it’s hard to annotate them. In this case, semi-supervised methods could solve this problem. However, standalone semi-supervised training does not take into account the differences between samples. We developed a new semi-supervised training method incorporating curriculum learning, where the model initially trains on easier samples and gradually progresses to more hard ones. This strategy could avoid the possible negative impact of the unlabeled samples in early stages in training and strengthen model’s robustness and serves as a plug- and-play approach and is adaptable to existing semi-supervised semantic segmentation methods. More concretely, we design a difficulty measurer to estimate the training difficulty of samples from the perspective of the entire dataset and a pace controller to control training time and duration of different samples according to their training difficulty. The experiment result shows the method we proposed could improves the performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Convolutional Neural Network Image Classification Method Integrating Classic Image Features 9

Convolutional Neural Network Image Classification Method Int...

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9th IEEE International Conference on Computational intelligence and applications, ICCIA 2024

作者： Hao, Qi Huang, Jie Wang, Bin Zhou, Feng Cyber Science and Engineering Southeast University Nanjing China Zhejiang Key Laboratory of Artificial Intelligence of Things Network and Data Security Hangzhou China

ISBN: (纸本)9798350352214

Classic image features were once widely used in image classification but have been almost entirely replaced by neural networks today. While the performance of neural networks, especially convolutional neural networks (CNNs), is indisputable, their lack of interpretability has become a significant limitation in recent years. This paper explores the effective combination of classic image features and convolutional image features, investigating their differences and similarities. Through a series of experiments, a method is proposed to effectively integrate these two types of features by employing a multiplicative attention mechanism. This approach combines standardized MPEG-7 descriptors with convolutional features before feeding them into the fully connected layer for classification. The final model demonstrates an improvement in classification accuracy and indicates the potential of learning traditional image features from the integrated features. © 2024 IEEE.

关键词： Convolutional neural networks

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MEG Channel Selection Using Copula Entropy-Based Transfer Entropy for Motor Imagery BCI 46

MEG Channel Selection Using Copula Entropy-Based Transfer En...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Tang, Chao Jiang, Dongyao Chen, Badong Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an China

ISBN: (纸本)9798350371499

Multi-channel magnetoencephalography (MEG) data provides high spatiotemporal resolution for motor imagery (MI)-based brain-machine interfaces (BCIs). However, not all channels contribute to the performance of BCIs. Taking into account the importance of specific channels in measuring their causal relationships with other channels during MI tasks, a novel channel selection method using copula entropy-based transfer entropy (CTE) is proposed to select task-relevant channels. Experiments on a publicly available dataset validate the effectiveness of the proposed methods. Compared to using all channels, channel selection based on CTE can significantly (p © 2024 IEEE.

关键词： Magnetoencephalography

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