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arXiv 2024年

作者： Zhao, Yong Zhu, Zhengqiu Gao, Chen Wang, En Huang, Jincai Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Hunan Province Changsha410073 China BNRist Tsinghua University Beijing100084 China College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Spatial Crowdsourcing (SC) is gaining traction in both academia and industry, with tasks on SC platforms becoming increasingly complex and requiring collaboration among workers with diverse skills. Recent research works address complex tasks by dividing them into subtasks with dependencies and assigning them to suitable workers. However, the dependencies among subtasks and their heterogeneous skill requirements, as well as the need for efficient utilization of workers’ limited work time in the multi-task allocation mode, pose challenges in achieving an optimal task allocation scheme. Therefore, this paper formally investigates the problem of Dependency-aware Multi-task Allocation (DMA) and presents a well-designed framework to solve it, known as Heterogeneous Graph Reinforcement Learning-based Task Allocation (HGRL-TA). To address the challenges associated with representing and embedding diverse problem instances to ensure robust generalization, we propose a multi-relation graph model and a Compound-path-based Heterogeneous Graph Attention Network (CHANet) for effectively representing and capturing intricate relations among tasks and workers, as well as providing embedding of problem state. The task allocation decision is determined sequentially by a policy network, which undergoes simultaneous training with CHANet using the proximal policy optimization algorithm. Extensive experiment results demonstrate the effectiveness and generality of the proposed HGRL-TA in solving the DMA problem, leading to average profits that is 21.78% higher than those achieved using the metaheuristic methods. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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Optical Flow Guided Pyramid Network for Video Salient Object Detection

Optical Flow Guided Pyramid Network for Video Salient Object...

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International Conference on computer Supported Cooperative Work in Design

作者： Tinglong Tang Sheng Hua Shuifa Sun Yirong Wu Yuqi Zhu Chonghao Yue Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China Yichang Key Laboratory of Intelligent Medicine China Three Gorges University Yichang China College of Computer and Information Technology China Three Gorges University Yichang China School of Information Science and Technology Hangzhou Normal University Hangzhou China School of Electrical Automation and Information Engineering Tianjin University Tianjin China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Video Salient Object Detection (VSOD) is a significant pre-work for many vision applications. Different for Salient Object Detection (SOD), an effective VSOD model requires not only the spatial domain of origin image but also temporal domain. In this paper, we proposed an optical flow guided pyramid network (OFPN) for VSOD, which exploit the temporal optical flow (OF) to assist VSOD. Due to the fact that optical flow maps have slightly lower quality compared to depth maps, we designed two modules for seeking better improvement. To this end, we render optical flow maps from RGB images firstly. Then, an adaptive cross-modal attention module (ACA) is designed for multi-modal fusion. The high-level encoded features are aggregated into a shared decoder for primary prediction. Besides, the low-level features are separately sent into multi-scale context attention module (MCA) for multi-scale context fusion with the assist of the primary prediction level by level. Further, we exploit a multi-scale loss to take full advantage of the hierarchical details through image pyramid structure. Extensive experiments on five benchmark datasets demonstrate the superiority of our method against 12 state-of-the-art methods.

关键词： Optical losses Uncertainty Accuracy Federated learning Object detection Benchmark testing Decoding

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SL3D-BF: A Real-World Structured Light 3D Dataset with Background-to-Foreground Enhancement

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Li, Yiming Chen, Weikang Li, Zinan Zhang, Chaobo Wang, Xiaohao Gui, Weihua Gao, Wen Liang, Xiaojun Li, Xinghui Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China Pengcheng Laboratory Shenzhen518000 China Central South University School of Automation Changsha410083 China Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing100871 China

Deep-learning-based structured light 3D reconstruction technology (SL3D) provides excellent solutions for intelligent manufacturing. However, the scarcity of real-world datasets covering full-process data and diverse objects hampers the validation of new ideas. Moreover, limited research on dataset construction strategies, such as scene backgrounds and sample distribution, reduces network performance. We investigate the impact of background stability on foreground accuracy (BS-FA) and find a whiteboard background improved foreground prediction accuracy by up to 82% over a black background. Guided by BS-FA, we develop the SL3D-BF, a background-effective SL3D dataset for industrial use, featuring approximately 2,100 scenes with diverse objects like metal/plastic workpieces, plaster sculptures, and standard parts for precise evaluation. It uniquely includes shadow and foreground masks, absent in prior datasets, and offers full-process data from gratings to 3D point clouds, totaling 100,800 gratings. We also establish an initial benchmark for future research by conducting evaluation experiments with advanced methods. Furthermore, we investigate the relationship between the spatial frequency of sample occurrence and the model predictive ability to minimize the time and resource demands of dataset construction. Most importantly, SL3D-BF is also a valuable resource for tasks like depth estimation, defect detection, and semantic segmentation. The dataset is available at: https://***/LiYiMingM/Dataset-SL3D-BF. © 1991-2012 IEEE.

关键词： Industrial research

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Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation Via Decoupling Optimization

SSRN

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SSRN 2023年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Visual Media Intelligent Processing Research Group Department of Film and Television Engineering Shanghai University Shanghai200072 China School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Youtu Lab Tencent China

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-label generation to select unlabeled points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Ex-✩Fully documented templates are available in the elsarticle package on CTAN. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Spectral-Spatial Fusion Hypergraph Convolutional Network for Multispectral Point Cloud Classification

Spectral-Spatial Fusion Hypergraph Convolutional Network for...

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Information Systems and Computing Technology (ISCTech), International Conference on

作者： Jiangbo Huang Qingwang Wang Zifeng Zhang Sivang Liu Zhifeng Wang Tao Shen Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China School of Information Engineering and Automation Kunming University of Science and Technology Kunming China General Logistics Department of PLA Real Estate Resource Management Center Beijing China Beijing Anlu International Technology Co Beijing China

Graph Neural Networks (GNNs) based on simple graph can directly model 3D multispectral point clouds, thus avoiding the problem of 3D spatial information loss when converting 3D point clouds to 2D feature images. However, considering the dense distribution of objects in remote sensing scenes, GNNs only consider the association between pairs of vertices, which is insufficient for exploring high-order correlations and long-term dependence correlations. Hypergraph neural network (HGNN) is a more generalized framework that overcomes the limitations of traditional simple graph. In this paper, we propose a spectral-spatial fusion hypergraph convolutional network (S2FHGCN) for 3D multispectral point cloud classification. S2FHGCN first generates two hypergraphs from multidimensional features including spectral feature and spatial feature, respectively. The hypergraph structure can better handle complex high-order correlations for representation learning. Then the two hypergraphs are fused to obtain global representation information. Finally, the multidimensional features and the fused hypergraph are input to the hypergraph convolution network. The experimental results on real multispectral point clouds demonstrate the effectiveness of our proposed S2FHGCN for multispectral point cloud classification.

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Novel Path Following for a Four-Wheel Steering Vehicle Based on Model Predictive Control

Novel Path Following for a Four-Wheel Steering Vehicle Based...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Rongqi Gu Tianhang Wang Bo Zhang Zhijun Li Tianhao Li Guang Chen Department of Automotive Engineering Tongji University Shanghai China Westwell-lab Shanghai China Tongji-Westwell Driverless Commercial Joint Laboratory Shanghai China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Automation University of Science and Technology of China Hefei China Department of Computer Science and Technology Tongji University Shanghai China

Path following is a crucial technique for ensuring the safe and efficient operation of automatic electric vehicles. Four-wheel steering (4WS) technology is known to enhance the accuracy and flexibility of such vehicles. In this paper, we propose a new constrained model predictive control (MPC) based method for path-following, specifically for 4WS vehicles. To simplify the 4WS vehicle kinematics model, we use the assumption of pure rolling and simplify it to a single-track model. We employ a high-precision linearization transformation to convert the nonlinear kinematics models to a linear control-state system. Subsequently, we design a new objective function based on the tracking error model, and formulate the control problem as an optimization problem. Finally, we convert the optimization problem into a quadratic programming (QP) form with constraints that are suitable for real-time applications. We demonstrate the effectiveness of our proposed control method through simulation experiments.

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A Novel Computational Nanobiosensing Approach to Improve the Exploitation of In Vivo Computation

A Novel Computational Nanobiosensing Approach to Improve the...

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Congress on Evolutionary Computation

作者： Shaolong Shi Yifan Chen Zhaoyang Jiang Qiang Liu Jurong Ding Qingfu Zhang Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou China School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Artificial Intelligence Key Laboratory of Sichuan Province Sichuan University of Science and Engineering Zigong China School of Automation and Information Engineering Sichuan University of Science and Engineering Zigong China

A novel nanobiosensing framework named “in vivo computation” has been proposed recently, where the challenge of early tumor detection is overcome from an optimization perspective. The biological gradient field (BGF) triggered by the tumor lesion is viewed as the optimizable objective function with the tumor site being the global optimum. The externally manipulable nanorobots playing the role of agents are manipulated in the search space (i.e., the vascular network of high-risk tissue). Several computational strategies have been proposed to realize tumor targeting by overcoming the in vivo constraints which focused on the tumor detection process without any emphasis on the nanorobots aggregation at the tumor. In this paper, we focus on the utilization rate of agents, which means to improve the percentage of nanorobots that detect the tumor site after it has been found by the first arrival agent (i.e., the nanorobot that detects the tumor at the earliest), and the solution set search, which means to find the tumor region as whole as possible. This process is interpreted as the exploitation process of in vivo computation. An exploitation approach named center-aided weak priority evolution strategy (CWP-ES) is developed for the setting of nanorobot moving direction in this paper. In the approach, a direction generated by the center of agents that have found the tumor mixed with the direction generated by the weak priority evolution strategy (WP-ES) proposed in the previous work is used to steer the motion of nanorobots that have not detected the tumor. Several numerical experiments are performed in a 3D search space to show the effectiveness of this novel computational nanobiosensing approach in three BGF landscapes with different degrees of optimization complexity.

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DiffusionRet: Generative Text-Video Retrieval with Diffusion Model

DiffusionRet: Generative Text-Video Retrieval with Diffusion...

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International Conference on computer Vision (ICCV)

作者： Peng Jin Hao Li Zesen Cheng Kehan Li Xiangyang Ji Chang Liu Li Yuan Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School Shenzhen China Department of Automation and BNRist Tsinghua University Beijing China Peng Cheng Laboratory Shenzhen China

Existing text-video retrieval solutions are, in essence, discriminant models focused on maximizing the conditional likelihood, i.e., p(candidates|query). While straightforward, this de facto paradigm overlooks the underlying data distribution p(query), which makes it challenging to identify out-of-distribution data. To address this limitation, we creatively tackle this task from a generative viewpoint and model the correlation between the text and the video as their joint probability p(candidates,query). This is accomplished through a diffusion-based text-video retrieval framework (Diffusion-Ret), which models the retrieval task as a process of gradually generating joint distribution from noise. During training, DiffusionRet is optimized from both the generation and discrimination perspectives, with the generator being optimized by generation loss and the feature extractor trained with contrastive loss. In this way, DiffusionRet cleverly leverages the strengths of both generative and discriminative methods. Extensive experiments on five commonly used text-video retrieval benchmarks, including MSRVTT, LSMDC, MSVD, ActivityNet Captions, and DiDeMo, with superior performances, justify the efficacy of our method. More encouragingly, without any modification, DiffusionRet even performs well in out-domain retrieval settings. We believe this work brings fundamental insights into the related fields. Code is available at https://***/jpthu17/DiffusionRet.

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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PQMPV: Parallel and Quantified Dense Stereo Disparity Estimation Based on Multi-Path Viterbi

PQMPV: Parallel and Quantified Dense Stereo Disparity Estima...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Rongqi Gu Tianhang Wang Tianpei Zou Bo Zhang Zhijun Li Haotian Zhang Guang Chen Department of Automotive Engineering Tongji University Shanghai China Westwell-lab Shanghai China Tongji-Westwell Driverless Commercial Joint Laboratory Shanghai China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Automation University of Science and Technology of China Hefei China Department of Computer Science and Technology Tongji University Shanghai China

Stereo depth estimation plays a crucial role in enabling robots, autonomous driving and other applications. As an ill-posed problem, both deep learning based and traditional methods have been extensively studied in recent years. Traditional methods mainly focus on feature matching between multiple images to estimate depth, but often result in blank pixels with zero disparity and also require significant computation time. In this paper, we propose a new stereo disparity estimation algorithm based on the dynamic programming method known as Viterbi. Our approach optimize the range of disparity calculation through image pyramid, combined with Viterbi algorithm for cost aggregation, and accelerates several calculation steps through parallel computing techniques. Additionally, we improve data transmission and processing speed by quantifying data from float to integer. Experiments on the KITTI dataset and MiddEval benchmark demonstrate that our algorithm achieves significant improvements in average error rate and processing speed of disparity map, making it a promising solution for stereo depth estimation in practical applications.

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