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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Leqi Zheng Tingting Zhu Xiao Ma School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

This paper presents a new joint source-channel coding (JSCC) scheme for wireless image transmission, which combines block compressed sensing (BCS) with systematic convolutional low density generator matrix (SC-LDGM) codes. In the proposed scheme (referred to as JSCC with BCS and SC-LDGM), an original image is firstly compressed by BCS and then quantized by dead-zone quantizer into ternary symbols. The resulting quantized sequence is encoded directly by a ternary SC-LDGM code and modulated by ternary pulse amplitude modulation (3-PAM). This scheme has the advantages to avoid the use of entropy coding and to match the quantized symbols with ternary coded modulations. Simulation results show that, the proposed scheme outperforms the conventional unequal error protection (UEP) scheme about 6 dB in terms of the energy-to-noise ratio (ENR).

关键词： Image coding Image reconstruction Wireless communication Image communication Compressed sensing Wireless sensor networks Channel coding

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Universal Physical Camouflage Attacks on Object Detectors

Universal Physical Camouflage Attacks on Object Detectors

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Lifeng Huang Chengying Gao Yuyin Zhou Cihang Xie Alan L. Yuille Changqing Zou Ning Liu School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Department of Computer Science The Johns Hopkins University Max Planck Institute for Informatics University of Maryland College Park

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

In this paper, we study physical adversarial attacks on object detectors in the wild. Previous works mostly craft instance-dependent perturbations only for rigid or planar objects. To this end, we propose to learn an adversarial pattern to effectively attack all instances belonging to the same object category, referred to as Universal Physical Camouflage Attack (UPC). Concretely, UPC crafts camouflage by jointly fooling the region proposal network, as well as misleading the classifier and the regressor to output errors. In order to make UPC effective for non-rigid or non-planar objects, we introduce a set of transformations for mimicking deformable properties. We additionally impose optimization constraint to make generated patterns look natural to human observers. To fairly evaluate the effectiveness of different physical-world attacks, we present the first standardized virtual database, AttackScenes, which simulates the real 3D world in a controllab.e and reproducible environment. Extensive experiments suggest the superiority of our proposed UPC compared with existing physical adversarial attackers not only in virtual environments (AttackScenes), but also in real-world physical environments.

关键词： Proposals Detectors Semantics Perturbation methods Strain Optimization Three-dimensional displays

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Dynamic NOMA-Based Computation Offloading in Vehicular Platoons

arXiv

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arXiv 2021年

作者： Zheng, Dongsheng Chen, Yingyang Wei, Lai Jiao, Bingli Hanzo, Lajos The Department of Electronics Peking University Beijing100871 China The College of Information Science and Technology Jinan University Guangzhou510632 China The Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Jinan University Guangzhou510632 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Both the Mobile edge computing (MEC)-based and fog computing (FC)-aided Internet of Vehicles (IoV) constitute promising paradigms of meeting the demands of low-latency pervasive computing. To this end, we construct a dynamic NOMA-based computation offloading scheme for vehicular platoons on highways, where the vehicles can offload their computing tasks to other platoon members. To cope with the rapidly fluctuating channel quality, we divide the timeline into successive time slots according to the channel's coherence time. Robust computing and offloading decisions are made for each time slot after taking the channel estimation errors into account. Considering a certain time slot, we first analytically characterize both the locally computed source data and the offloaded source data as well as the energy consumption of every vehicle in the platoons. We then formulate the problem of minimizing the long-term maximum task queue by optimizing the allocation of both the communication and computing resources. To solve the problem formulated, we design an online algorithm based on the classic Lyapunov optimization method and successive convex approximation (SCA) method. Finally, the numerical simulation results characterize the performance of our algorithm and demonstrate its advantages both over the local computing scheme and the orthogonal multiple access (OMA)-based offloading scheme. © 2021, CC BY.

关键词： Computation offloading

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Cross-modal consensus network for weakly supervised temporal action localization

arXiv

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arXiv 2021年

作者： Hong, Fa-Ting Feng, Jia-Chang Xu, Dan Shan, Ying Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Peng Cheng Laboratory Shenzhen China Tencent PCG Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science and Engineering HKUST Hong Kong Pazhou Lab Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to localize action instances in the given video with video-level categorical supervision. Previous works use the appearance and motion features extracted from pre-trained feature encoder directly, e.g., feature concatenation or score-level fusion. In this work, we argue that the features extracted from the pre-trained extractors, e.g., I3D, which are trained for trimmed video action classification, but not specific for WS-TAL task, leading to inevitable redundancy and sub-optimization. Therefore, the feature re-calibration is needed for reducing the task-irrelevant information redundancy. Here, we propose a cross-modal consensus network (CO2-Net) to tackle this problem. In CO2-Net, we mainly introduce two identical proposed cross-modal consensus modules (CCM) that design a cross-modal attention mechanism to filter out the task-irrelevant information redundancy using the global information from the main modality and the cross-modal local information from the auxiliary modality. Moreover, we further explore inter-modality consistency, where we treat the attention weights derived from each CCM as the pseudo targets of the attention weights derived from another CCM to maintain the consistency between the predictions derived from two CCMs, forming a mutual learning manner. Finally, we conduct extensive experiments on two commonly used temporal action localization datasets, THUMOS14 and ActivityNet1.2, to verify our method, which we achieve the state-of-the-art results. The experimental results show that our proposed cross-modal consensus module can produce more representative features for temporal action localization. Copyright © 2021, The Authors. All rights reserved.

关键词： Carbon dioxide

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Improving fast segmentation with teacher-student learning 29

Improving fast segmentation with teacher-student learning

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29th British Machine Vision Conference, BMVC 2018

作者： Xie, Jiafeng Shuai, Bing Hu, Jian-Fang Lin, Jingyang Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China Nanyang Technological University Singapore Guangdong Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing MOE Hong Kong

Recently, segmentation neural networks have been significantly improved by demonstrating very promising accuracies on public benchmarks. However, these models are very heavy and generally suffer from low inference speed, which limits their application scenarios in practice. Meanwhile, existing fast segmentation models usually fail to obtain satisfactory segmentation accuracies on public benchmarks. In this paper, we propose a teacher-student learning framework that transfers the knowledge gained by a heavy and better performed segmentation network (i.e. teacher) to guide the learning of fast segmentation networks (i.e. student). Specifically, both zero-order and first-order knowledge depicted in the fine annotated images and unlab.led auxiliary data are transferred to regularize our student learning. The proposed method can improve existing fast segmentation models without incurring extra computational overhead, so it can still process images with the same fast speed. Extensive experiments on the Pascal Context, Cityscape and VOC 2012 datasets demonstrate that the proposed teacher-student learning framework is able to significantly boost the performance of student network. © 2018. The copyright of this document resides with its authors.

关键词： Students

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DONet: Dual-octave network for fast MR image reconstruction

arXiv

引用

arXiv 2021年

作者： Feng, Chun-Mei Yang, Zhanyuan Fu, Huazhu Xu, Yong Yang, Jian Shao, Ling Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China School of Automation Engineering University of Electronic Science and Technology of China 611731 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates PCA Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjiang University of Science and Technology Nanjiang210094 China Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration has long been the subject of research. This is commonly achieved by obtaining multiple undersampled images, simultaneously, through parallel imaging. In this paper, we propose the Dual-Octave Network (DONet), which is capable of learning multi-scale spatialfrequency features from both the real and imaginary components of MR data, for parallel fast MR image reconstruction. More specifically, our DONet consists of a series of Dual-Octave convolutions (Dual-OctConv), which are connected in a dense manner for better reuse of features. In each Dual-OctConv, the input feature maps and convolutional kernels are first split into two components (i.e., real and imaginary), and then divided into four groups according to their spatial frequencies. Then, our Dual-OctConv conducts intra-group information updating and inter-group information exchange to aggregate the contextual information across different groups. Our framework provides three appealing benefits: (i) It encourages information interaction and fusion between the real and imaginary components at various spatial frequencies to achieve richer representational capacity. (ii) The dense connections between the real and imaginary groups in each Dual-OctConv make the propagation of features more efficient by feature reuse. (iii) DONet enlarges the receptive field by learning multiple spatial-frequency features of both the real and imaginary components. Extensive experiments on two popular datasets (i.e., clinical knee and fastMRI), under different undersampling patterns and acceleration factors, demonstrate the superiority of our model in accelerated parallel MR image reconstruction. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Performance of Systematic Convolutional Low Density Generator Matrix Codes over Rayleigh Fading Channels with Impulsive Noise 3rd

Performance of Systematic Convolutional Low Density Generato...

引用

3rd International Conference on Space information Networks, SINC 2018

作者： Ji, Meiying Chen, Shengxiao Ma, Xiao School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China

ISBN: (纸本)9789811359361

We investigate the systematic convolutional low density generator matrixÂ (SC-LDGM) codes over Rayleigh fading channels with symmetric alpha-stableÂ (SαS) impulsive noise. The performance is analyzed by deriving a lower bound based on an equivalent genie-aidedÂ (GA) system. Numerical simulations show that the SC-LDGM codes can achieve a significant gain compared to the convolutional codes over Rayleigh fading channels with impulsive noise. Numerical results also show that the performance of the SC-LDGM codes can be around one dB away from Shannon limits at the bit-error rateÂ (BER) of 10-5 and matches well with the GA lower bound in the low BER region. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Impulse noise

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Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

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Chemosphere 2024年 352卷 141329页

作者： Yaseen, Zaher Mundher Melini Wan Mohtar, Wan Hanna Homod, Raad Z. Alawi, Omer A. Abba, Sani I. Oudah, Atheer Y. Togun, Hussein Goliatt, Leonardo Ul Hassan Kazmi, Syed Shabi Tao, Hai Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Membranes and Water Security King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Department of Civil Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia UKM Selangor Bangi 43600 Malaysia Environmental Management Centre Institute of Climate Change Universiti Kebangsaan Malaysia Selangor UKM Bangi 43600 Malaysia Department of Oil and Gas Engineering Basrah University for Oil and Gas Basra Iraq Department of Thermofluids School of Mechanical Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru 81310 Malaysia Department of Computer Sciences College of Education for Pure Science University of Thi-Qar Nasiriyah 64001 Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq Department of Mechanical Engineering College of Engineering University of Baghdad Baghdad Iraq Computational and Applied Mechanics Department Federal University of Juiz de Fora 36036-900 Brazil Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention and Guangdong Provincial Key Laboratory of Marine Biotechnology Shantou University Shantou 515063 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Institute of Big Data Application and Artificial Intelligence Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Faculty of Data Science and Information Technology INTI International University 71800 Malaysia

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies. © 2024 Elsevier Ltd

关键词： Artificial intelligence Heavy metals Sensitivity analysis Soil contamination

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Auto-Generating Neural Networks with Reinforcement Learning for Multi-Purpose Image Forensics

Auto-Generating Neural Networks with Reinforcement Learning ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yujun Wei Yifang Chen Xiangui Kang Z. Jane Wang Liang Xiao Guangdong Key Lab of Information Security School of Data and Computer Science Sun Yat-Sen University Guangzhou China Department of ECE University of British Colombia Vancouver Canada Department of Communication Engineering Xiamen University Xiamen China

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

Designing a forensic convolutional neural network (CNN) is usually based on some ad-hoc intuition and domain knowledge. Many methods to automate neural network design have been proposed for computer vision tasks, but they may not be directly applied to image forensic problems, which tend to detect weak traces signals left by image operations rather than strong image content signals. In this paper, we propose an approach to learn an optimal forensic CNN structure with reinforcement learning for detecting multiple image tampering operations. A learning agent is introduced to select CNN layers sequentially in a limited state-action space using Q-learning with an $\epsilon$-greedy strategy and experience replay. The experiments demonstrate that the auto-generated network performs better than other classic image forensic methods and shows more robustness against JPEG compression. To our knowledge, this is the first attempt to design forensic deep neural networks automatically with reinforcement learning.

关键词： Image forensics Learning (artificial intelligence) Task analysis Neural networks Feature extraction Transform coding

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Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection

Interactive Two-Stream Decoder for Accurate and Fast Salienc...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Huajun Zhou Xiaohua Xie Jian-Huang Lai Zixuan Chen Lingxiao Yang School of Data and Computer Science Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Recently, contour information largely improves the performance of saliency detection. However, the discussion on the correlation between saliency and contour remains scarce. In this paper, we first analyze such correlation and then propose an interactive two-stream decoder to explore multiple cues, including saliency, contour and their correlation. Specifically, our decoder consists of two branches, a saliency branch and a contour branch. Each branch is assigned to learn distinctive features for predicting the corresponding map. Meanwhile, the intermediate connections are forced to learn the correlation by interactively transmitting the features from each branch to the other one. In addition, we develop an adaptive contour loss to automatically discriminate hard examples during learning process. Extensive experiments on six benchmarks well demonstrate that our network achieves competitive performance with a fast speed around 50 FPS. Moreover, our VGG-based model only contains 17.08 million parameters, which is significantly smaller than other VGG-based approaches. Code has been made availab.e at: https://***/moothes/ITSD-pytorch.

关键词： Correlation Saliency detection Task analysis Decoding Linear programming Silicon Benchmark testing

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