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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Liang, Jingguo Yi, Ru Chen, Jicheng Nie, Yiming Zhang, Hui School of Transportation Engineering and Science Beihang University Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China National Innovation Institute of Defense Technology Beijing P.R. China

Autonomous vehicles (AVs) heavily depend on machine learning-based algorithms for the purpose of environmental perception. However, extensively utilized deep learning-based visual perception methods are susceptible to adversarial visual sensor attacks, thereby introducing latent risks to vehicle driving safety. In this paper, we first investigate a novel adversarial patch attack method for the DNN-based visual object detection method widely used in AVs. The proposed adversarial attack strategy contains an evasion attack mode and a misclassified attack mode. For each mode, the corresponding adversarial patch generation method is designed by a dedicated optimization process. In addition, we propose an adversarial attack defense method based on texture feature and local denoising. According to the texture features, the method can detect the locations of the possible adversarial patches in the image, and then the image is denoised locally from the detection results. The attack effects of the generated adversarial patch are validated on the KITTI dataset and in real driving scenes, respectively. The results show that the average accuracy of car and pedestrian detection under evasion attacks decreases by 56% and 68%, respectively, while under misclassification attacks, the average accuracy decreases by 35% and 85% for car and pedestrian, respectively. The proposed method can effectively mislead the target detection model which seriously affects the perception module. This paper conduct a defense experiment using data generated by the adversarial patch, resulting in a 92% increase in mean average precision for detecting cars and pedestrians, with detection time reduced to 17% of its original value. The experimental results demonstrate the defensive effectiveness and efficiency of the proposed defense method. IEEE

关键词： Autonomous vehicles

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Parallel Implementation of the CCSDS Turbo Decoder on GPU

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China Communications 2024年第10期21卷 70-77页

作者： Liu Zhanxian Liu Rongke Zhang Haijun Wang Ning Sun Lei Wang Jianquan School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China

This paper presents a software turbo decoder on graphics processing units(GPU).Unlike previous works,the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space data Systems(CCSDS)***,the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism *** mitigate this issue,we propose a padding method that inserts several bits before the information frame *** obtain low-latency performance and high resource utilization,two-level intra-frame parallelisms and an efficient data structure are *** presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution(LTE)turbo codes with only small *** proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50Mbps throughputs for the code rates 1/6 and 1/2,respectively.

关键词： CCSDS CUDA GPU parallel decoding turbo codes

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IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 868-881页

作者： He, Ziqiang Wan, Shaohua Zappatore, Marco Lu, Hu School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China Shenzhen Institute forAdvanced Study University of Electronic Science and Technology of China Shenzhen518110 China Department of Engineering for Innovation University of Salento Lecce73100 Italy

In recent years, multiview clustering algorithms have achieved promising performance by exploiting the complementarity and consistency of different views. However, many multiview spectral clustering methods only focus on the consistent information of views, and the time cost of feature decomposition is expensive. Moreover, these methods also require postprocessing (e.g., k-means) to obtain the final clustering results. To overcome these limitations simultaneously, we propose a novel multiview clustering algorithm. First, the method removes the inconsistent information of the views through cross-view measurement to maintain consistent information. These inconsistencies may be caused by noise, corruptions, or view-specific properties and will affect the quality of the similarity matrix. Then, we learn a consensus embedding matrix with nonnegative constraints by performing a low-rank decomposition of the consistency information. In this way, we can replace the eigendecomposition of the n bm × n Laplacian matrix in spectral clustering with the singular value decomposition of a n bm ×c low-rank matrix to reduce the computational burden, where c bm ll n. Furthermore, due to the nonnegative constraint, we can directly obtain the clustering results. Also, to consider the diversity of views, adaptive weighting is applied to different view data. Compared to state-of-the-art multiview clustering methods on five benchmark multiview datasets, we demonstrate the superiority and effectiveness of our approach. We release the source code at https://***/hulu88/FAMvC. © 2023 IEEE.

关键词： Embeddings

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Deep Multi-View Clustering via View-Specific Representation and Global Graph 7

Deep Multi-View Clustering via View-Specific Representation ...

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7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Pu, Jingyu Cui, Chenhang Ren, Yazhou Wen, Zichen Wu, Tianyi Pu, Xiaorong He, Lifang University of Electronic Science And Technology of China School of Computer Science And Engineering China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Lehigh University Department of Computer Science And Engineering Bethlehem United States

ISBN: (纸本)9798350359145

Multi-view clustering can improve clustering performance by leveraging the complementary information from multiview data and has garnered growing interest. Despite significant advancements has been established in this field, most existing methods primarily concentrate on learning the latent representation, neglecting the latent graph that typically reveals relationships between different instances. To address this limitation, this paper presents a new framework of Deep Multi-View Clustering via View-specific Representation and Global Graph (DVGMVC) to address the aforementioned issue. Our method leverages the latent graph of each view to enhance representation quality and incorporates a global graph that combines discriminative information to enhance the performance of deep embedded clustering. Specifically, we employ autoencoders to learn embedded features and construct latent graphs from each view. Subsequently, a weighted two-layer plain residual network refines the latent feature using topological information derived from the latent graphs. As various views adhere to the same foundational cluster structure across multiple views, DVGMVC merges the embedded features of all views to create a unified feature. By leveraging the Graph Convolutional Network (GCN) module, which exploits information from both the latent graphs and the global graph generated from the global feature, a unified embedded feature is generated to capture comprehensive topological information among samples. This unified embedded feature is then employed to generate pseudo labels in a self-supervised manner, guiding unified embedding learning and clustering. In a self-supervised manner, such pseudo-label information guides unified embedding learning and clustering. In-depth experiments on public datasets has proven the state-of-the-art clustering effectiveness of our proposed method. © 2023 IEEE.

关键词： Embeddings

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An intelligent active probing and trace-back scheme for IoT anomaly detection

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Digital Communications and Networks 2024年第1期10卷 168-181页

作者： Luying Wang Lingyi Chen Neal N.Xiong Anfeng Liu Tian Wang Mianxiong Dong School of Computer Science and Engineering Central South UniversityChangsha 410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA Artificial Intelligence and Future Networks Beijing Normal University&UICZhuhai 519087China Department of Information and Electronic Engineering Muroran Institute of TechnologyMuroran 050-8585Japan

Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data *** Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33%respectively,while the accuracy of trust identification is improved by 20%.

关键词： Anomaly detection Internet of things Integrating data collection Mobile edge users Intelligent

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PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pipelining 42

PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pi...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Shi, Shaohuai Pan, Xinglin Chu, Xiaowen Li, Bo Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Hong Kong Baptist University Department of Computer Science Hong Kong Data Science and Analytics Thrust Hong Kong The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Large models have attracted much attention in the AI area. The sparsely activated mixture-of-experts (MoE) technique pushes the model size to a trillion-level with a sub-linear increase of computations as an MoE layer can be equipped with many separate experts, but only one or two experts need to be trained for each input data. However, the feature of dynamically activating experts of MoE introduces extensive communications in distributed training. In this work, we propose PipeMoE to adaptively pipeline the communications and computations in MoE to maximally hide the communication time. Specifically, we first identify the root reason why a higher pipeline degree does not always achieve better performance in training MoE models. Then we formulate an optimization problem that aims to minimize the training iteration time. To solve this problem, we build performance models for computation and communication tasks in MoE and develop an optimal solution to determine the pipeline degree such that the iteration time is minimal. We conduct extensive experiments with 174 typical MoE layers and two real-world NLP models on a 64-GPU cluster. Experimental results show that our PipeMoE almost always chooses the best pipeline degree and outperforms state-of-the-art MoE training systems by 5%-77% in training time. © 2023 IEEE.

关键词： Pipelines

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Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment 2

Transformer-Based Classification Outcome Prediction for Mult...

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2nd IEEE International Conference on Sensors, electronics and computer engineering, ICSECE 2024

作者： Ma, Danqing Wang, Meng Xiang, Ao Qi, Zongqing Yang, Qin Computer Science Stevens Institute of Technology HobokenNJ United States University of Sofia Department of Computer Science Palo AltoCA United States School of Computer Science & Engineering University of Electronic Science and Technology of China Sichuan China School of Integrated Circult Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Stevens Institute of Technology Computer Science HobokenNJ United States

ISBN: (纸本)9798350373646

This study proposes a multi-modal fusion framework Multitrans based on the Transformer architecture and self-attention mechanism. This architecture combines the study of non-contrast computed tomography (NCCT) images and discharge diagnosis reports of patients undergoing stroke treatment, using a variety of methods based on Transformer architecture approach to predicting functional outcomes of stroke treatment. The results show that the performance of single-modal text classification is significantly better than single-modal image classification, but the effect of multi-modal combination is better than any single modality. Although the Transformer model only performs worse on imaging data, when combined with clinical meta-diagnostic information, both can learn better complementary information and make good contributions to accurately predicting stroke treatment effects. © 2024 IEEE.

关键词： computerized tomography

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Clinical Text Classification Using Sequential Forward Selection 2

Clinical Text Classification Using Sequential Forward Select...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and electronics, ICIITCEE 2024

作者： Rajasekaran, Rajkumar Sanghavi, Veer Solanki, Rashi Masih, Jolly Vellore Institute of Technology Department of Analytics Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Bml Munjal University Department of Business Analytics Gurugram India

ISBN: (纸本)9798350306415

Clinical text classification is a critical undertaking within the realm of Natural Language Processing, holding significant implications for healthcare applications. In this NLP project, our primary objective is the categorization of medical transcripts into their corresponding medical conditions. To accomplish this, we harness the power of Sequential Forward Selection (SFS), a feature selection technique meticulously chosen for its capacity to streamline data dimensions. By employing SFS, our project aspires to not only enhance classification performance but also elevate the efficiency of pattern recognition, ensuring both speed and accuracy in disease identification. This research contribution underscores the pivotal role of Clinical Text Classification, with a specific focus on optimizing the process using SFS. © 2024 IEEE.

关键词： Clinical Text Classification Medical transcripts Sequential Forward Selection

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A Digital-Twin-Empowered Lightweight Model-Sharing Scheme for Multirobot Systems

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IEEE Internet of Things Journal 2023年第19期10卷 17231-17242页

作者： Xiong, Kai Wang, Zhihong Leng, Supeng He, Jianhua University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

Multirobot system for manufacturing is an Industry Internet of Things (IIoT) paradigm with significant operational cost savings and productivity improvement, where unmanned aerial vehicles (UAVs) are employed to control and implement collaborative productions without human intervention. This mission-critical system relies on 3-dimension (3-D) scene recognition to improve operation accuracy in the production line and autonomous piloting. However, implementing 3-D point cloud learning, such as Pointnet, is challenging due to limited sensing and computing resources equipped with UAVs. Therefore, we propose a digital twin (DT) empowered knowledge distillation (KD) method to generate several lightweight learning models and select the optimal model to deploy on UAVs. With a digital replica of the UAVs preserved at the edge server, the DT system controls the model-sharing network topology and learning model structure to improve recognition accuracy further. Moreover, we employ network calculus to formulate and solve the model sharing configuration problem toward minimal resource consumption, as well as convergence. Simulation experiments are conducted over a popular point cloud data set to evaluate the proposed scheme. Experiment results show that the proposed model-sharing scheme outperforms the individual model in terms of computing resource consumption and recognition accuracy. © 2014 IEEE.

关键词： Multipurpose robots

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Multi-Task Learning for UAV Trajectory and Caching With Federated Cloud-Assisted Knowledge Distillation

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2516-2533页

作者： Tietaa Maale, Gerald Arden Elorm Kuadey, Noble Arafat, Yeasin Thomas, Kwantwi Sun, Guolin Liu, Guisong Research Institute of Intelligent Computing University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China McCoy University College of Education Department of Mathematics and ICT Nadowli Ghana Research Institute of Intelligent Computing at the University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China Ho Technical University Department of Computer Science Ho Ghana University of Mines and Technology Department of Computer Science and Engineering Tarkwa Ghana Southwestern University of Finance and Economics Complex Laboratory of New Finance and Economics School of Computing and Artificial Intelligence Chengdu610074 China Ministry of Education Engineering Research Center of Intelligent Finance Chengdu610030 China

The proliferation of Internet of Things (IoT) technologies and ubiquitous connectivity has led to uncrewed aerial vehicles (UAVs) playing key role as edge servers, revolutionizing the wireless communications landscape by facilitating computing and caching resources closer to ground users (GUs). This advancement significantly alleviates core network loads, reduces latency, and guarantees content availability even in congested or remote areas. However, jointly optimizing UAV caching strategies and trajectories gives rise to a multi-task optimization (MTO) problem. This paper introduces a novel multi-task geo-temporal caching (MT-GTC) framework that addresses the interplay between UAV caching mechanisms and trajectory optimization in a cohesive manner. Leveraging a proposed multi-task learning (MTL) model for joint optimization of UAV caching and trajectory design, we develop a federated learning cloud-assisted knowledge distillation (FL-CAKD) scheme to preserve data privacy and adapt to data heterogeneity. FL-CAKD transfers knowledge from a cloud model orchestrator (CMO), which houses a large and sophisticated teacher model, to a lightweight on-device MTL student models using soft target distributions instead of large model parameters, significantly reducing communication costs. MT-GTC optimizes caching and trajectories to maximize cache hits and minimize latency. Evaluations on real-world mobility datasets demonstrate up to 95% cache hit rates and 21% lower delays compared to baselines. © 2004-2012 IEEE.

关键词： Multi-task learning

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