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EC²Detect: Real-Time Online Video Object Detection in Edge-Cloud Collaborative IoT

IEEE internet of Things Journal 2022年第20期9卷 20382-20392页

作者： Siyan Guo Cong Zhao Guiqin Wang Jiaqing Yang Shusen Yang National Engineering Laboratory for Big Data Analytics and the School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China Department of Computing Imperial College London London U.K. National Engineering Laboratory for Big Data Analytics and the School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Industrial Artificial Intelligence Center Pazhou Laboratory Guangzhou China

Video object detection is a fundamental technology of intelligent video analytics for internet of Things (IoT) applications. However, even with extraordinary detection accuracy, predominating solutions based on deep convolutional neural networks (DCNNs) cannot achieve real-time online object detection on video streams with a low end-to-end (E2E) response latency and therefore cannot be applied to proliferating latency-sensitive IoT applications like autonomous driving requiring large-scale intelligent video analytics. To address this issue, we present EC2Detect, an edge-cloud collaborative real-time online video object detection method. Specifically, we propose a tracking-assisted object detection architecture based on edge-cloud collaboration with keyframe selection, where the accurate but heavy object detection is conducted by the Cloud on sparse keyframes adaptively selected according to their semantic variation, and the lightweight object tracking is used to localize and identify objects in other frames at edge devices. Extensive experiments of our real-world prototype demonstrate that, EC2Detect significantly outperforms state-of-the-art methods in terms of processing speed (up to $4.77\times $ faster), E2E latency (up to $8.12 \times $ lower), and edge-cloud bandwidth occupation ( $17 \times $ lower) with an acceptable mAP, which can effectively support large-scale intelligent video analytics in practice. Source code of EC2Detect is available at https://***/ECCDetect/ECCDetect .

关键词： Object detection Image edge detection Streaming media internet of Things Collaboration Visual analytics Feature extraction

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A Dynamic Multi-Objective Evolutionary Algorithm Based on Niche Prediction Strategy

SSRN

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SSRN 2022年

作者： Zheng, Jinhua Zhang, Bo Zou, Juan Yang, Shengxiang Hu, Yaru Hunan Engineering Research Center of Intelligent System Optimization and Security Xiangtan University Hunan Province Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Xiangtan University Hunan Province Xiangtan411105 China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

In reality, many multi-objective optimization problems are dynamic. The Pareto optimal front (PF) or Pareto optimal solution (PS) of these dynamic multi-objective problems (DMOPs) changes as the environment change. Therefore, solving such problems requires an optimization algorithm that can quickly track the PF or PS after an environment change. Prediction-based response mechanism is a common method used to deal with environmental changes, which is commonly known as center point-based prediction. However, if the predicted direction of the center point is inaccurate, the predicted population will be biased towards one side. In this paper, we propose a niche prediction strategy based on center and boundary points (PCPB) to solve the dynamic multi-objective optimization problems, which consists of three steps. After environmental changes are detected, the first step is to divide the niche, dividing different individuals in the PS into different niche populations. The second step is to independently predict different niches, and select individuals with good convergence and distribution in the niche to predict the individuals that will produce the next generation. Finally, some different individuals are randomly generated in the next possible PS area to ensure the diversity of the population. To verify whether our proposed strategy is effective and competitive, PCPB was compared with five state-of-the-art strategies. The experimental results show that PCPB performed competitively in solving dynamic multi-objective optimization problems, which proves that our algorithm has good competitiveness. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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ZTEI: Zero-Trust and Edge Intelligence Empowered Continuous Authentication for Satellite Networks

ZTEI: Zero-Trust and Edge Intelligence Empowered Continuous ...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Peiyu Fu Jun Wu Xi Lin Ao Shen School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China Collaborative Innovation Center of Shanghai Industrial Internet Shanghai China Faculty of Science and Engineering Waseda University Japan Department of Computer Science Faculty of Engineering The University of Hong Kong Hong Kong China

ISBN: (纸本)9781665435413

The integration of satellite communication technology and terrestrial infrastructure has resulted in an un-precedented increase in network services covering the world. The main effect of the rapid growth of satellite networks is a broader range of data exchange and business interaction between the internal and external systems, making the network boundaries blur or even disappear. As a result, traditional passive security mechanisms based on dividing network boundaries cannot provide sufficient protection. To address this issue, in this paper, we propose a zero-trust and edge intelligence (ZTEI) empowered continuous authentication for satellite networks. We build an improved zero-trust architecture (ZTA) for satellite networks, which expands the traditional zero-trust concept to the multi-dimensional zero-trust that focuses on subject, object, environment, behavior, and physical entity. Then we propose a continuous authentication scheme in the proposed zero-trust architecture, enabling proactive and continuous authentication by periodically monitoring and re-evaluating variable attributes throughout the request lifecycle. Besides, in this scheme, we also design a Neural-Backed Decision Trees (NBDTs) based edge intelligence algorithm to improve the authentication accuracy. Finally, we build a testbed to evaluate the performance of the proposed architecture. Compared with the attribute-based access control (ABAC) under the traditional zero-trust architecture, our proposed architecture can improve the authentication accuracy of dynamic illegal requests by about 27%. In addition, according to standard network performance evaluation criteria, the loss of processing performance caused by our solution is also within an acceptable range.

关键词： Performance evaluation Satellites Heuristic algorithms Authentication Zero Trust Global communication Decision trees

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Improved Diversity-Promoting Collaborative Metric Learning for Recommendation

arXiv

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arXiv 2024年

作者： Bao, Shilong Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Security Department of Alibaba Group Hangzhou311121 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and collaborative filtering. Following the convention of RS, existing practices exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to introduce a set of multiple representations for each user in the system where users' preference toward an item is aggregated by taking the minimum item-user distance among their embedding set. Specifically, we instantiate two effective assignment strategies to explore a proper quantity of vectors for each user. Meanwhile, a Diversity Control Regularization Scheme (DCRS) is developed to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could induce a smaller generalization error than traditional CML. Furthermore, we notice that CML-based approaches usually require negative sampling to reduce the heavy computational burden caused by the pairwise objective therein. In this paper, we reveal the fundamental limitation of the widely adopted hard-aware sampling from the One-Way Partial AUC (OPAUC) perspective and then develop an effective sampling alternative for the CML-based paradigm. Finally, comprehensive experiments over a range of benchmark datasets speak to the efficacy of DPCML. Code are available at https://***/statusrank/LibCML. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Amfmer: A Multimodal Full Transformer for Unifying Aesthetic Assessment Tasks ⋆

SSRN

引用

SSRN 2023年

作者： Qi, Jin Su, Can Hu, Xiao-Xuan Chen, Meng-Wei Sun, Yan-Fei Dong, Zhen-Jiang Cao, Yue Liu, Tian-Liang Luo, Jie-Bo College of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing210046 China Jiangsu ToYou Research Institute of Information Intelligence and Technology Nanjing210012 China College of Telecommunications and Information Nanjing University of Posts and Telecommunications Nanjing210003 China Department of Computer Science University of Rochester RochesterNY14627 United States Institute of System Security and Trusted Computing School of Cyber science and Engineering Wuhan University Wuhan430072 China

Computational aesthetics aims to simulate the human visual perception process via the computers to automatically evaluate aesthetic quality with automatic methods. This topic has been widely studied by numerous researchers. However, existing research mostly focuses on image content while disregarding high-level semantics in the related image comments. In addition, most major assessment methods are based on convolutional neural networks (CNNs) for learning the distinctive features, which lack representational power and modeling capabilities for multimodal assessment requirement. Furthermore, many transformer-based model approaches suffer from limited information ffow between different parts of the assumed model, and aesthetic prior knowledge may, to some extent, limit the ability of the given model to perceive image style and atmosphere. Inspired by the above questions, in this paper, A novel Multimodal Full transforMER (AMFMER) evaluation model without prior knowledge is proposed, consisting of three components: visual stream, textual stream and multimodal fusion layer. Firstly, the visual stream exploits the improved Swin transformer to extract the distinctive layer features of the input image. Secondly, the textual stream is based on the robustly optimized bidirectional encoder representations from transformers (RoBERTa) text encoder to extract semantic information from the corresponding comments. Thirdly, the multimodal fusion layer fuses visual features, textual features and low-layer salient features in a cross-attention manner to extract the multimodal distinctive features. Experimental results show that the proposed AMFMER approach in this paper outperforms current mainstream methods in a uniffed aesthetic prediction task, especially in terms of the correlation between the objective model evaluation and subjective human evaluation. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

关键词：

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YOLO-TP: A process detection model of tunnel real scene based on gradient information interaction

YOLO-TP: A process detection model of tunnel real scene base...

引用

computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Jiaying Fan Shuping Pan Xinbo Jiang Shengtao Zhang Hongliang Liu Chuan Wang Jianghua Li Chenglin Ke Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Technological Innovation Department Shandong Hi-Speed Group Co Ltd JiNan China School of Qilu Transportation Shandong University JiNan China China Railway 14th Bureau Group First Engineering Development Co. Ltd RiZhao China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Target detection technology is an effective method to detect tunnel operation safety. Although the field has made some progress in detection models, the lack of datasets and detection models has hindered its development. We produce and propose Tunnel Processes data (TP), a challenging dataset of high-precision real-world tunnel process target detection that reduces the limitations of research in this field. TP has a high resolution: 1920×1080, 1734images,9 categories. TP is the first tunnel process data set under construction to date, and its fidelity exceeds that of other tunnel process datasets in the field of target detection. In addition, we also propose a YOLO-TP benchmark model to make up for the lack of a high-precision tunnel process image target detection baseline model. Experiments on TP datasets demonstrate their validity, and experiments on the dataset presented in this paper demonstrate the validity of the benchmark model.

关键词： Accuracy Image resolution Computational modeling Object detection Benchmark testing Big Data Feature extraction Data models Safety Artificial intelligence

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Consistency and Diversity induced Human Motion Segmentation

arXiv

引用

arXiv 2022年

作者： Zhou, Tao Fu, Huazhu Gong, Chen Shao, Ling Porikli, Fatih Ling, Haibin Shen, Jianbing The PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of High Performance Computing Agency for Science Technology and Research Singapore Riyadh Saudi Arabia The Research School of Engineering The Australian National University Australia The Department of Computer Science Stony Brook University Strony Brook NY United States The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University United States

Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster data without guidance from prior knowledge, resulting in unsatisfactory segmentation results. To this end, we propose a novel Consistency and Diversity induced human Motion Segmentation (CDMS) algorithm. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces, in which transfer subspace learning is conducted on different layers to capture multi-level information. A multi-mutual consistency learning strategy is carried out to reduce the domain gap between the source and target data. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance. Moreover, to preserve the temporal correlations, an enhanced graph regularizer is imposed on the learned representation coefficients and the multi-level representations of the source data. The proposed model can be efficiently solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experimental results on public human motion datasets demonstrate the effectiveness of our method against several state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion analysis

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ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection

arXiv

引用

arXiv 2022年

作者： Shen, Yiyang Yu, Rongwei Wu, Peng Xie, Haoran Gong, Lina Qin, Jing Wei, Mingqiang The Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China The School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Department of Computing and Decision Sciences Lingnan University Hong Kong Hong Kong The School of Nursing The Polytechnic University of Hong Kong Hong Kong Hong Kong

LiDAR and camera, as two different sensors, supply geometric (point clouds) and semantic (RGB images) information of 3D scenes. However, it is still challenging for existing methods to fuse data from the two cross sensors, making them complementary for quality 3D object detection (3OD). We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sensor discrepancies by progressively fusing the multi-scale features of camera Images and LiDAR point clouds. ImLiDAR enables to provide the detection head with cross-sensor yet robustly fused features. To achieve this, two core designs exist in ImLiDAR. First, we propose a cross-sensor dynamic message propagation module to combine the best of the multi-scale image and point features. Second, we raise a direct set prediction problem that allows designing an effective set-based detector to tackle the inconsistency of the classification and localization confidences, and the sensitivity of hand-tuned hyperparameters. Besides, the novel set-based detector can be detachable and easily integrated into various detection networks. Comparisons on both the KITTI and SUN-RGBD datasets show clear visual and numerical improvements of our ImLiDAR over twenty-three state-of-the-art 3OD methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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GSLB: The Graph Structure Learning Benchmark

arXiv

引用

arXiv 2023年

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

关键词： Benchmarking

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