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International Conference on Computer science & Education (ICCSE)

作者： Li Qin Ci Zhang Bo-Xuan Fang Tian Wu Chong-Zhi Shong College of informatics HuaZhong Agricultural University WuHan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

With the development of robotics, computer vision and artificial intelligence, the study of Plant phenotyping has entered a stage of rapid growth. For robots can be used for a large-scale, automatic and sustainable phenotype collection and data processing, it has also been followed closely by more and more research institutions and international seed giants, but currently the more mature phenotype robots are huge in size and expensive in configuration. Although their accuracy are high, but the popularity are poor, so it is difficult to popularize on a large scale, which is not conducive to collect plant phenotype data in a wider space and for the more plant varieties. On the other side, the small phenotype robots have low cost, simple operation, and are more suitable for large-scale promotion. The current research on plant phenotype small robots is mainly based on small wheeled robots. The robot is equipped with visual and optical sensors for collecting plant information, and uses machine vision and various sensors to achieve the robot's movement, positioning and obstacle avoidance. This paper uses the small wheeled mobile robot to simulate the navigation and obstacle avoidance of the phenotype collecting robot in parallel walls, and its effectiveness is proved by simulation experiment and real machine test.

关键词： Mobile robots Agriculture Imaging Robot sensing systems Navigation

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Underwater vision clearing with latent space alignment for marine scene enhancement

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Displays 2025年 90卷

作者： Yifan Liu Chuanbo Zhu Ke Luo Jincai Chen School of Electrical and Electronic Engineering Hubei University of Technology Wuhan 430068 Hubei China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 Hubei China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan 430074 Hubei China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 Hubei China

Color shift and marine snow are two severe types of underwater imaging noise, which can significantly impact subsequent computer vision tasks. However, existing methods cannot perform underwater image enhancement (UIE) and marine snow removal (MSR) simultaneously with a unified network architecture and a set of pretrained weights. To address this issue, we propose the two-stage joint UIE and MSR network (TJUMnet). With the proposed latent space alignment-based teacher-guided feature optimization (TFO) and hierarchical reconstruction (HR), TJUMnet can effectively integrate and learn feature representations under various underwater noise conditions. In the first stage, TFO utilizes a well-trained teacher model to guide the primary student model through the latent space feature optimization (LSFO) module, reducing differences between feature domains and enhancing robustness against underwater noise. The second stage, HR, aims to extend and generalize the processing capability for various underwater noise conditions, enabling the student model to achieve stable reconstruction and enhancement effects under different underwater noise conditions. Additionally, we constructed a new joint underwater-image enhancement and marine-snow removal dataset (JUEMR), comprising 2700 image sets covering color shift noise and different intensities of marine snow noise. Extensive experiments on multiple benchmark datasets and the JUEMR dataset demonstrate that TJUMnet reaches state-of-the-art levels in terms of quantitative and visual performance. The code is available at https://***/awhitewhale/TJUMnet .

关键词： Snow and ice removal

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Auction based clustered federated learning in mobile edge computing system

arXiv

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arXiv 2021年

作者： Lu, Renhao Zhang, Weizhe Li, Qiong Zhong, Xiaoxiong Vasilakos, Athanasios V. School of Computer Science and Technology Harbin Institute of Technology Harbin China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen China School of Electrical and Data Engineering University of Technology Sydney Australia Department of Computer Science and Technology Fuzhou University Fuzhou350116 China Department of Computer Science Electrical and Space Engineering Lulea University of Technology Lulea97187 Sweden

In recent years, mobile clients’ computing ability and storage capacity have greatly improved, efficiently dealing with some applications locally. Federated learning is a promising distributed machine learning solution that uses local computing and local data to train the Artificial Intelligence (AI) model. Combining local computing and federated learning can train a powerful AI model under the premise of ensuring local data privacy while making full use of mobile clients’ resources. However, the heterogeneity of local data, that is, Non-independent and identical distribution (Non-IID) and imbalance of local data size, may bring a bottleneck hindering the application of federated learning in mobile edge computing (MEC) system. Inspired by this, we propose a cluster-based clients selection method that can generate a federated virtual dataset that satisfies the global distribution to offset the impact of data heterogeneity and proved that the proposed scheme could converge to an approximate optimal solution. Based on the clustering method, we propose an auction-based clients selection scheme within each cluster that fully considers the system’s energy heterogeneity and gives the Nash equilibrium solution of the proposed scheme for balance the energy consumption and improving the convergence rate. The simulation results show that our proposed selection methods and auction-based federated learning can achieve better performance with the Convolutional Neural Network model (CNN) under different data distributions. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing

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ANIMC: A soft approach for auto-weighted noisy and incomplete multi-view clustering

arXiv

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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multi-view clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multi-view clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this paper, we propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering approach (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model. Firstly, by designing adaptive semi-regularized nonnegative matrix factorization (adaptive semi-RNMF), the soft auto-weighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Secondly, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results demonstrate its superior advantages over other state-of-the-art methods. Impact Statement-As an effective method to process data from multiple sources, multi-view clustering has attracted more and more attention. However, most previous works ignore missing instances and noises in original multi-view data, which limits the applications of these works. By a soft approach, our proposed ANIMC can effectively reduce the negative influence of missing instances and noises. Moreover, ANIMC outperforms the stateof- the-art works by about 20% in representative cases. With satisfactory performance on multiple rea

关键词： Regression analysis

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QoE Models for Online Video Streaming

QoE Models for Online Video Streaming

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International Conference on Ubiquitous Computing and Communications and International Symposium on Cyberspace and security (IUCC-CSS)

作者： Rongqi Zhang Xu Zhang Peiyao Guo Qilin Fan Hao Yin Zhan Ma School of Electronic Science and Engineering Nanjing University China Mathematics & Physical Sciences College of Engineering University of Exeter UK School of Big Data and Software Engineering Chongqing University China Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing University China Beijing National Research Center for Information Science and Technology Tsinghua University China

ISBN: (纸本)9781665466684

With the rising popularity of video streaming services, Quality of Experience (QoE) acts as the crucial role in improving user's experience. Distinguished from Quality of Service (QoS), which mainly weigh the video streaming quality with network transmission performance, QoE focuses on the subjective and user-oriented assessment when users views the videos online. It is a vital issue for online streaming to ensure a user-satisfied transmission under dynamic network. This highlights the need for an accurate and feasible QoE model to balance the user experience and available transmission bandwidth. This paper summaries and analyzes existing explorations on QoE models for online video streaming, especially with the advancement on emerged learning-based models, to promote the development of this research field.

关键词： Analytical models Computational modeling Quality of service Bandwidth Streaming media Ubiquitous computing User experience

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Fine-grained Scheduling in FPGA-Based Convolutional Neural Networks

Fine-grained Scheduling in FPGA-Based Convolutional Neural N...

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IEEE International Conference on Cloud Computing and big data Analysis (ICCCBDA)

作者： Wei Zhang Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160252

FPGA has been considered as a promising solution to accelerate Convolutional Neural Networks (CNNs) for its excellent performance in energy efficiency and programmability. However, prior designs are usually designed for inference only as designers can map pre-trained models to the hardware in a very efficient way. However, those approaches may not be suitable for training CNN models. In this paper, we propose FConv, in which the CPU and FPGA work together in a fine-grained manner. The FPGA accelerator in FConv uses one Winograd-based convolver, which reduces the design complexity and improves performance. We apply double-buffer for output routine to effectively overlap computation and data transfer. We also integrate multiple PEs to improve data parallelism. We propose our analytical model for prediction and use it as a guide in task scheduling. We find the upper limit of performance under the current design based on the analytical model. We evaluate our design on VGG-16 and Densnet-40 on ImageNet and CIFAR-10. We achieve 262.43 GOP/s on the VGG-16 model, which is 2.13× of the performance compared to FFT-based implementation on the same platform. We also achieve at most 4×+ performance improvement compared MKL with 20 threads running on 10 core Intel processors.

关键词： Field programmable gate arrays Computational modeling Training Convolution Acceleration Bandwidth Task analysis

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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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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Optimal Margin Distribution Machine with Sparsity Inducing Penalty

Optimal Margin Distribution Machine with Sparsity Inducing P...

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International Conference on big data and Smart Computing (bigCOMP)

作者： Teng Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Recently a promising research direction of statistical learning has been advocated, i.e., the optimal margin distribution learning, with the central idea of optimizing the margin distribution. As the most representative approach of this new learning paradigm, the optimal margin distribution machine (ODM) considers maximizing the margin mean and minimizing the margin variance simultaneously. The standard ODM exploits the ℓ_2-norm penalty, which gives rise to a dense decision boundary. However, in some situations, the model with parsimonious representation is more preferred, due to the redundant noisy features or limited computing resources. In this paper, we propose the sparse optimal margin distribution machine (Sparse ODM), which aims to achieve better generalization performance with moderate model size. For optimization, we extends an efficient coordinate descent method to solve the final problem since the variables are decoupled. In each iteration, we propose a modified Newton method to solve the one-variable sub-problem. Experimental results on both synthetic and real data sets show the superiority of the proposed method.

关键词： Standards Newton method Noise measurement Optimization Support vector machines Computational modeling Training

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Unbalanced incomplete multi-view clustering via the scheme of view evolution: Weak views are meat;strong views do eat

arXiv

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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous methods assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using previous methods. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view clustering problem;2) it designs the low-rank representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods. We provide codes for all of our experiments in https://***/ZeusDavide/TETCI_UIMC. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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