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Perspectives on cross-domain visual analysis of cyber-physical-social big data

Frontiers of Information Technology & Electronic Engineering 2021年第12期22卷 1559-1564页

作者： Wei CHEN Tianye ZHANG Haiyang ZHU Xumeng WANG Yunhai WANG The State Key Lab of CAD Sz CG Zhejiang UniversityHangzhou 310058China School of Computer Science and Technology Shandong UniversityJinan 250100China

The domain of cyber-physical-social(CPS)big data is generally defined as the set consisting of all the elements in its defined domain,including domains of data,objects,tasks,application scenarios,and *** analytics is an emerging human-in-the-loop big data analytics paradigm that can exploit human perception to enhance human cognitive efficiency.

关键词： Visual visual efficiency.

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Discovery of novel DprE1 inhibitors via computational bioactivity fingerprints and structure-based virtual screening

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中国药理学报（英文版） 2022年第6期43卷 1605-1615页

作者： Xue-ping Hu Liu Yang Xin Chai Yi-xuan Lei Shah Alam Lu Liu Chao Shen De-jun Jiang Zhe Wang Zhi-yong Liu Lei Xu Kang-lin Wan Tian-yu Zhang Yue-lan Yin Dan Li Dong-sheng Cao Ting-jun Hou Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China State Key Laboratory of Respiratory Disease Guangzhou Institutes of Biomedicine and HealthChinese Academy of SciencesGuangzhou 510530China University of Chinese Academy of Sciences Beijing 100049China Xiangya School of Pharmaceutical Sciences Central South UniversityChangsha 410013China Institute of Bioinformatics and Medical Engineering School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhou 213001China State Key Laboratory of Infectious Disease Prevention and Control Collaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesNational Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 102206China Jiangsu Key Laboratory of Zoonosis Yangzhou UniversityYangzhou 225009China

Decaprenylphosphoryl-β-D-ribose oxidase(DprE1)plays important roles in the biosynthesis of mycobacterium cell ***1 inhibitors have shown great potentials in the development of new regimens for tuberculosis(TB)*** this study,an integrated molecular modeling strategy,which combined computational bioactivity fingerprints and structure-based virtual screening,was employed to identify potential DprE1 *** lead compounds(B2 and H3)that could inhibit DprE1 and thus kill Mycobacterium smegmatis in vitro were ***,compound H3 showed potent inhibitory activity against Mycobacterium tuberculosis in vitro(MICMtb=1.25 μM)and low cytotoxicity against mouse embryo fibroblast NIH-3T3 *** research provided an effective strategy to discover novel anti-TB lead compounds.

关键词： molecular docking virtual screening DprE1 tuberculosis covalent inhibitors

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Contour detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field

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Research in Astronomy and Astrophysics 2020年第1期20卷 83-92页

作者： Xi-Qi Yang Qing-Feng Zhang Zhan Li Department of Computer Science Jinan UniversityGuangzhou 510632China State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310000China Sino-French Joint Laboratory for Astrometry Dynamics and Space ScienceJinan UniversityGuangzhou 510632China

In Cassini ISS(Imaging science Subsystem)images,contour detection is often performed on disk-resolved objects to accurately locate their ***,contour detection is a key *** edge detection methods,such as Canny and Roberts,often extract the contour with too much interior details and *** the deep convolutional neural network has been applied successfully in many image tasks,such as classification and object detection,it needs more time and computer *** this paper,a contour detection algorithm based on H-ELM(Hierarchical Extreme Learning Machine)and Dense CRF(Dense Conditional Random Field)is proposed for Cassini ISS *** experimental results show that this algorithm’s performance is better than both traditional machine learning methods,such as Support Vector Machine,Extreme Learning Machine and even deep Convolutional Neural *** extracted contour is closer to the actual ***,it can be trained and tested quickly on the general configuration of PC,and thus can be applied to contour detection for Cassini ISS images.

关键词： techniques:image processing methods:data analysis astrometry

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An Architecture Modeling Framework for Distributed Automation Systems Using SysML and Semantic Web Technologies

An Architecture Modeling Framework for Distributed Automatio...

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IEEE International Conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Dan Wang Xiaofeng Li Bin Gu Yue Cao Yusheng Liu Academy of Aerospace Propulsion Technology Innovation Center for Liquid Rocket Engine Digital Research and Development CNSA Xian China Beijing Institute of Control Engineering Beijing China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China State Key Lab. Of CAD&CG Zhejiang University Hangzhou China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

The rising interdisciplinarity and complexity of the Distributed Automation Systems (DASs) require the systems to be modeled in an unambiguous and high-level abstract way for cross-discipline/stage communication and interoperability in the Model-Driven Development (MDD) lifecycle. The concept of the System Architecture Model in systems engineering has been adopted for this challenge. To support the creation and analysis of this model, a modeling framework with a modeling methodology, modeling language, knowledge base, and related toolkit is established based on SysML and Semantic Web Technologies. A modeling methodology which is the core of the framework for modeling the architecture of DASs is formally defined with domain-specificity, comprehensiveness, discipline-neutrality, and platform-independency. Based on it, the SysML-DAS modeling language is extended from SysML, and the System Architecture Ontology is built with the help of the Knowledge Extraction Tool. This ontology works as the knowledge base not only to provide a unified and unambiguous view of the system but also to support the automated accomplishment of tasks in the MDD process. As a typical task, the semantic correctness and integrity of the system architecture model can be assessed by the Knowledge Analysis Tool in this framework.

关键词： Semantic Web Analytical models Automation Mechatronics Knowledge based systems Semantics Systems architecture Ontologies Service-oriented architecture Model-driven development

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Learning Photometric Feature Transform for Free-Form Object Scan

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IEEE Transactions on Visualization and computer Graphics 2024年 PP卷 PP页

作者： Feng, Xiang Kang, Kaizhang Pei, Fan Ding, Huakeng You, Jinjiang Tan, Ping Zhou, Kun Wu, Hongzhi Zhejiang University State Key Lab of CAD & CG Hangzhou310058 China King Abdullah University of Science and Technology Visual Computing Center Thuwal23955-6900 Saudi Arabia The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering SAR Hong Kong Hong Kong

We propose a novel framework to automatically learn to aggregate and transform photometric measurements from multiple unstructured views into spatially distinctive and view-invariant low-level features, which are subsequently fed to a multi-view stereo pipeline to enhance 3D reconstruction. The illumination conditions during acquisition and the feature transform are jointly trained on a large amount of synthetic data. We further build a system to reconstruct both the geometry and anisotropic reflectance of a variety of challenging objects from hand-held scans. The effectiveness of the system is demonstrated with a lightweight prototype, consisting of a camera and an array of LEDs, as well as an off-the-shelf tablet. Our results are validated against reconstructions from a professional 3D scanner and photographs, and compare favorably with state-of-the-art techniques. © 1995-2012 IEEE.

关键词： Photometry

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DashBot: Insight-Driven Dashboard Generation Based on Deep Reinforcement Learning

arXiv

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arXiv 2022年

作者： Deng, Dazhen Wu, Aoyu Qu, Huamin Wu, Yingcai The State Key Lab of CAD&CG Zhejiang University China The Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Analytical dashboards are popular in business intelligence to facilitate insight discovery with multiple charts. However, creating an effective dashboard is highly demanding, which requires users to have adequate data analysis background and be familiar with professional tools, such as Power BI. To create a dashboard, users have to configure charts by selecting data columns and exploring different chart combinations to optimize the communication of insights, which is trial-and-error. Recent research has started to use deep learning methods for dashboard generation to lower the burden of visualization creation. However, such efforts are greatly hindered by the lack of large-scale and high-quality datasets of dashboards. In this work, we propose using deep reinforcement learning to generate analytical dashboards that can use well-established visualization knowledge and the estimation capacity of reinforcement learning. Specifically, we use visualization knowledge to construct a training environment and rewards for agents to explore and imitate human exploration behavior with a well-designed agent network. The usefulness of the deep reinforcement learning model is demonstrated through ablation studies and user studies. In conclusion, our work opens up new opportunities to develop effective ML-based visualization recommenders without beforehand training datasets. © 2022, CC BY.

关键词： Reinforcement learning

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Minilag Filter for Jitter Elimination of Pose Trajectory in AR Environment

Minilag Filter for Jitter Elimination of Pose Trajectory in ...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Xiuqiang Song Weijian Xie Jiachen Li Nan Wang Fan Zhong Guofeng Zhang Xueying Qin School of Software Shandong University Engineering Research Center of Digital Media Technology Ministry of Education P.R. China State Key Lab of CAD&CG Zhejiang University SenseTime Research Tetras.AI School of Computer Science and Technology Shandong University

In AR applications, the jitter of virtual objects can weaken the sense of integration with the real environment. This jitter is often caused by noise in the pose obtained by 3D tracking or localization methods, especially in monocular vision systems without IMU support. Filtering the pose is an effective method to eliminate jitter, however, it can also cause significant lag in the filtered pose, seriously degrading the AR experience. Existing filters struggle to simultaneously reduce jitter while maintaining low lag. In this paper, we propose a novel Minilag filter, which achieves excellent pose smoothing while significantly reducing the lag through backtracking update and compensation strategies, and has excellent real-time performance. We represent the rotation in the pose in the Lie algebra and filter it in locally Euclidean space, ensuring that the filtering of rotation is consistent with that of vectors. We also analyze the noise distribution and characteristics in the tracked pose, providing a theoretical basis for setting filter parameters. We evaluated the proposed filter using both objective mathematical metrics and a user study, and the experimental results demonstrate that our method achieves state-of-the-art performance.

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ChartKG: A Knowledge-Graph-Based Representation for Chart Images

arXiv

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arXiv 2024年

作者： Zhou, Zhiguang Wang, Haoxuan Zhao, Zhengqing Zheng, Fengling Wang, Yongheng Chen, Wei Wang, Yong School of Media and Design Hangzhou Dianzi University CO Hangzhou310018 China School of Media and Design Hangzhou Dianzi University China Big Data Visualization and Human Computer Collaborative Intelligent Laboratory Hangzhou Dianzi University China Research Center of Big Data Intelligence Zhejiang Lab Hangzhou China State Key Lab of CAD&CG Zhejiang University China College of Computing and Data Science Nanyang Technological University Singapore

Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, which can benefit downstream tasks like chart retrieval and knowledge graph completion. However, existing methods for chart knowledge mining mainly focus on converting chart images into raw data and often ignore their visual encodings and semantic meanings, which can result in information loss for many downstream tasks. In this paper, we propose ChartKG, a novel knowledge graph (KG) based representation for chart images, which can model the visual elements in a chart image and semantic relations among them including visual encodings and visual insights in a unified ***, we develop a general framework to convert chart images to the proposed KG-based representation. It integrates a series of image processing techniques to identify visual elements and relations, e.g., CNNs to classify charts, yolov5 and optical character recognition to parse charts, and rule-based methods to construct graphs. We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts, and further demonstrate how our approach can benefit downstream applications such as semantic-aware chart retrieval and chart question answering. We also conduct quantitative evaluations to assess the two fundamental building blocks of our chart-to-KG framework, i.e., object recognition and optical character recognition. The results provide support for the usefulness and effectiveness of ChartKG. © 2024, CC BY.

关键词： Knowledge graph

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Automatic pose and wrinkle transfer for aesthetic garment display

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computer Aided Geometric Design 2021年 89卷 102020-102020页

作者： Wang, Luyuan Li, Honglin Xiao, Qinjie Yao, Xinran Pan, Xiaoyu Zhang, Yuqing Jin, Xiaogang State Key Lab of CAD&CG Zhejiang University Hangzhou310058 China Quanzhou Medical College China

We present an automatic and semantic pose and wrinkle transfer method from one garment onto another for aesthetic display, which is previously performed by professional artists using a knowledge-intensive and time-consuming process. Given a source garment model with fine wrinkle details in a specific pose and another target garment model with a similar style in a neutral pose but without fine wrinkle details, our approach can automatically transfer the pose and wrinkle details faithfully from the source to the target using a two-stage process. In the semantic correspondence establishment stage, we construct a dense correspondence between the source and the target by utilizing their semantic information in 2D patterns. Specifically, we first obtain the initial correspondence points on the paired 2D patterns by leveraging their semantic information. These marker points, which act as constraints, are mapped to their corresponding 3D models. We then establish their per-triangle correspondence using a non-rigid Iterative Closest Point (ICP) algorithm. In the deformation transfer stage, we transfer the pose and wrinkle details from the source to the target by solving an optimization problem. Extensive experiments validate that our method is able to generate better results compared to state-of-the-art methods, and it can lead to significant time savings for fashion designers. © 2021 Elsevier B.V.

关键词： Deformation

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Interactive Rendering of Relightable and Animatable Gaussian Avatars

arXiv

引用

arXiv 2024年

作者： Zhan, Youyi Shao, Tianjia Wang, He Yang, Yin Zhou, Kun State Key Lab of CAD & CG Zhejiang University Hangzhou310058 China Department of Computer Science University College London Gower Street LondonWC1E 6BT United Kingdom Kahlert School of Computing University of Utah United States

Creating relightable and animatable avatars from multi-view or monocular videos is a challenging task for digital human creation and virtual reality applications. Previous methods rely on neural radiance fields or ray tracing, resulting in slow training and rendering processes. By utilizing Gaussian Splatting, we propose a simple and efficient method to decouple body materials and lighting from sparse-view or monocular avatar videos, so that the avatar can be rendered simultaneously under novel viewpoints, poses, and lightings at interactive frame rates (6.9 fps). Specifically, we first obtain the canonical body mesh using a signed distance function and assign attributes to each mesh vertex. The Gaussians in the canonical space then interpolate from nearby body mesh vertices to obtain the attributes. We subsequently deform the Gaussians to the posed space using forward skinning, and combine the learnable environment light with the Gaussian attributes for shading computation. To achieve fast shadow modeling, we rasterize the posed body mesh from dense viewpoints to obtain the visibility. Our approach is not only simple but also fast enough to allow interactive rendering of avatar animation under environmental light changes. Experiments demonstrate that, compared to previous works, our method can render higher quality results at a faster speed on both synthetic and real datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Animation

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