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arXiv 2021年

作者： Peng, Yang Liu, Ping Luo, Yawei Zhou, Pan Xu, Zichuan Liu, Jingen Center for Frontier AI Research Agency for Science Technology and Research Singapore Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Liaoning Dalian China

Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value. In past years, by following the clustering and finetuning paradigm, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets. Inspired by recent teacher-student framework based methods, which try to mimic the human learning process either by making the student directly copy behavior from the teacher or selecting reliable learning materials, we propose to conduct further exploration to imitate the human learning process from different aspects, i.e., adaptively updating learning materials, selectively imitating teacher behaviors, and analyzing learning materials structures. The explored three components, collaborate together to constitute a new method for unsupervised domain adaptive person re-identification, which is called Human Learning Imitation framework. The experimental results on three benchmark datasets demonstrate the efficacy of our proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Students

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Encoding physics to learn reaction-diffusion processes

arXiv

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arXiv 2021年

作者： Rao, Chengping Ren, Pu Wang, Qi Buyukozturk, Oral Sun, Hao Liu, Yang Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Department of Mechanical and Industrial Engineering Northeastern University BostonMA02115 United States Department of Civil and Environmental Engineering Northeastern University BostonMA02115 United States Department of Civil and Environmental Engineering MIT CambridgeMA02139 United States Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing100872 China School of Engineering Sciences University of Chinese Academy of Sciences Beijing101408 China

Modeling complex spatiotemporal dynamical systems, such as the reaction-diffusion processes, have largely relied on partial differential equations (PDEs). However, due to insufficient prior knowledge on some under-explored dynamical systems, such as those in chemistry, biology, geology, physics and ecology, and the lack of explicit PDE formulation used for describing the nonlinear process of the system variables, to predict the evolution of such a system remains a challenging task. Unifying measurement data and our limited prior physics knowledge via machine learning provides us with a new path to solving this problem. Existing physics-informed learning paradigms impose physics laws through soft penalty constraints, whose solution quality largely depends on a trial-and-error proper setting of hyperparameters. Since the core of such methods is still rooted in black-box neural networks, the resulting model generally lacks interpretability and suffers from critical issues of extrapolation and generalization. To this end, we propose a deep learning framework that forcibly encodes given physics structure to facilitate the learning of the spatiotemporal dynamics in sparse data regimes. We show how the proposed approach can be applied to a variety of problems regarding the PDE system, including forward and inverse analysis, data-driven modeling, and discovery of PDEs. The resultant learning paradigm that encodes physics shows high accuracy, robustness, interpretability and generalizability demonstrated via extensive numerical experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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Automatic Recognition of Business Process Images

Automatic Recognition of Business Process Images

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2019 IEEE International Conference on Computer Science and Educational Informatization, CSEI 2019

作者： Kang, Qingyi Yu, Yong Dai, Fei School of Software Yunnan University Kunming650091 China School of Software Key Laboratory in Software Engineering of Yunnan Province Yunnan University Kunming650091 China School of Big Data and Intelligent Engineering Southwest Forestry University Kunming650224 China

ISBN: (纸本)9781728123080

The Business Process Modelling Notation (BPMN) is a defacto standard for modelling business processes in the early phases of systems development. The increased adoption of business process management approaches has led organizations to model large collections of BPMN business process models. The mix of constructs in BPMN business process models can lead to semantic error. The ability to take a picture of BPMN business process models and analyze the semantic correctness of BPMN business process images is thus a desirable feature. In this paper, we propose an automatic recognition method to transform BPMN business process images into Petri nets, for which efficient analysis techniques are available. The proposed recognition has been implemented as a tool, in conjunction with existing Petri net-based tools, that enables the soundness analysis of business process images. This transformation can led to the identification of deficiencies in the BPMN business process images. © 2019 IEEE.

关键词： Petri nets

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Corrigendum to Intrusion detection based on improved density peak clustering for imbalanced data on sensor-cloud systems Journal of Systems Architecture volume 118 (2021) 102212

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Journal of Systems Architecture 2021年 119卷

作者： Ming Yan Yewang Chen Xiaoliang Hu Dongdong Cheng Yi Chen Jixiang Du The College of Computer Science and Technology Huaqiao University Xiamen China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing China

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Private Trajectory data Publication for Trajectory Classification 16th

Private Trajectory Data Publication for Trajectory Classific...

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16th Web Information Systems and Applications Conference, WISA 2019

作者： Zhu, Huaijie Yang, Xiaochun Wang, Bin Wang, Leixia Lee, Wang-Chien School of Data and Computer Science Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computer Science and Engineering Northeastern University Shenyang China The Pennsylvania State University State College United States

ISBN: (纸本)9783030309510

Trajectory classification (TC), i.e., predicting the class labels of moving objects based on their trajectories and other features, has many important real-world applications. Private trajectory data publication is to anonymize trajectory data, which can be released to the public or third parties. In this paper, we study private trajectory publication for trajectory classification (PTPTC), which not only preserves the trajectory privacy, but also guarantees high TC accuracy. We propose a private trajectory data publishing framework for TC, which constructs an anonymous trajectory set for publication and use in data services to classify the anonymous trajectories. In order to build a "good" anonymous trajectory set (i.e., to guarantee a high TC accuracy), we propose two algorithms for constructing anonymous trajectory set, namely Anonymize-POI and Anonymize-FSP. Next, we employ Support Vector Machine (SVM) classifier to classify the anonymous trajectories. Finally, the experimental results show that our proposed algorithms not only preserve the trajectory privacy, but also guarantee a high TC accuracy. © 2019, Springer Nature Switzerland AG.

关键词： Trajectories

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SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET

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PeerJ Computer Science 2025年 11卷 e2623-e2623页

作者： Abbas, Hyder Ren, Shen Bing Asim, Muhammad Hassan, Syeda Iqra El-Latif, Ahmed A. Abd State Key Laboratory of Public Big Data College of Computer Science and Technology Institute for Artificial Intelligence Guizhou University Guizhou Guiyang China School of Computer Science and Engineering Central South University Changsha China EIAS Data Science and Blockchain Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering British Malaysian Institute Universiti of Kuala Lumpur Kuala Lumpur Malaysia Software Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. To address this challenge posed by complex backgrounds in salient object detection is crucial for advancing the field. This article proposes a novel deep learning-based architecture called SODU2-NET (Salient object detection U2-Net) for salient object detection that utilizes the U-NET base structure. This model addresses a gap in previous work that focused primarily on complex backgrounds by employing a densely supervised encoder-decoder network. The proposed SODU2-NET employs sophisticated background subtraction techniques and utilizes advanced deep learning architectures that can discern relevant foreground information when dealing with complex backgrounds. Firstly, an enriched encoder block with full feature fusion (FFF) with atrous spatial pyramid pooling (ASPP) varying dilation rates to efficiently capture multi-scale contextual information, improving salient object detection in complex backgrounds and reducing the loss of information during down-sampling. Secondly the block includes an attention module that refines the decoder, is constructed to enhances the detection of salient objects in complex backgrounds by selectively focusing attention on relevant features. This allows the model to reconstruct detailed and contextually relevant information, which is essential to determining salient objects accurately. Finally, the architecture has been improved by adding a residual block at the encoder end, which is responsible for both saliency prediction and map refinement. The proposed network is designed to learn the transformation between input images and ground truth, enabling accurate segmentation of salient object regions with clear borders and accurate prediction of fine structures. SODU2-NET is demonstrated to have superior performance in five public datasets, including DUTS, SOD, DUT OMRON, HKU-IS, PASCA

关键词： Object detection

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An improved method of algal-bloom discrimination in Taihu Lake using Sentinel-1A data 6

An improved method of algal-bloom discrimination in Taihu La...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Wu, Lin Sun, Mengwei Min, Lin Zhao, Jianhui Li, Ning Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China

ISBN: (纸本)9781728129129

The algal bloom is a prominent manifestation of water pollution. Synthetic aperture radar (SAR) shows an advantage in water monitoring due to its characteristic of all-time and all-weather. The water regions where algae gather present dark in SAR image. However, dark regions may also be caused by other factors, such as low wind. This paper proposes an improved algal bloom discrimination method based on Artificial Neural Network (ANN) to recognize the dark regions of algal bloom. Taihu Lake is chosen as the research area in this study because of its serious bloom in recent years. By means of quasi-synchronous optical images, the dark region database of SAR images labeled algal bloom and non-algal bloom are obtained. Then the segmentation algorithm and region growing algorithm are used to acquire the feature from dark regions, and divided into training feature set and testing feature set. Finally, the training and testing feature set are used for ANN-based discrimination model construction and verification. According the experimental results, the overall accuracy reaches 80%, which indicates that ANN model has a good applicability in algal bloom recognition of SAR image. © 2019 IEEE.

关键词： Neural networks

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Delay-Optimal Edge Cache Replacement with Non-Markovian Content Fetching

Delay-Optimal Edge Cache Replacement with Non-Markovian Cont...

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GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Nannan Zhang Wei Wang Pan Zhou Aiping Huang College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

Leveraging the content caching technology, popular contents can be in close proximity to the users, which reduces the delay effectively. In this paper, we dedicate to minimizing the long-term average delay for the cache-enabled wireless networks, in which the fetching delay is considered when the uncached contents are retrieved from remote servers. We construct a decision-theoretic framework for this delay-optimal content-caching problem. The most challenging obstacle is that the consideration of imperfect content fetching breaks the Markovian property in the decision-theoretic framework, which makes the problem difficult to solve. To overcome this obstacle, we propose an equivalent transformation by analyzing the queue dynamics during the content fetching delay, so that the transformed problem can be modeled as an infinite horizon semi-Markov decision process (SMDP). To handle the curse of dimensionality for solving the SMDP, we decompose the global optimality equation into several per-content equations, and propose a low-complexity delay-optimal content caching algorithm. Simulation results show that the proposed algorithm achieves significantly lower delay than conventional caching algorithms.

关键词： Delays Servers Mathematical model Wireless networks Queueing analysis Optimization Time measurement

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Fashion Editing With Adversarial Parsing Learning

Fashion Editing With Adversarial Parsing Learning

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haoye Dong Xiaodan Liang Yixuan Zhang Xujie Zhang Xiaohui Shen Zhenyu Xie Bowen Wu Jian Yin School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China School of Intelligent Systems Engineering Sun Yat-sen University Petuum Inc. ByteDance AI Lab

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value. Existing works often treat it as a general inpainting task and do not fully leverage the semantic structural information in fashion images. Moreover, they directly utilize conventional convolution and normalization layers to restore the incomplete image, which tends to wash away the sketch and color information. In this paper, we propose a novel Fashion Editing Generative Adversarial Network (FE-GAN), which is capable of manipulating fashion images by free-form sketches and sparse color strokes. FE-GAN consists of two modules: 1) a free-form parsing network that learns to control the human parsing generation by manipulating sketch and color; 2) a parsing-aware inpainting network that renders detailed textures with semantic guidance from the human parsing map. A new attention normalization layer is further applied at multiple scales in the decoder of the inpainting network to enhance the quality of the synthesized image. Extensive experiments on high-resolution fashion image datasets demonstrate that the proposed FE-GAN significantly outperforms the state-of-the-art methods on fashion image manipulation.

关键词： Image color analysis Semantics Convolution Colored noise Feature extraction Task analysis Neural networks

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Global context-aware progressive aggregation network for salient object detection

arXiv

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arXiv 2020年

作者： Chen, Zuyao Xu, Qianqian Cong, Runmin Huang, Qingming University of Chinese Academy of Sciences Beijing China Key Lab. of Intelligent Information Processing ICT CAS Beijing China Institute of Information Science Beijing Jiaotong University Beijing China Key Lab. of Big Data Mining and Knowledge Management CAS Beijing China Peng Cheng Laboratory Shenzhen Guangdong China

Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiplelevel feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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