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Erratum to "Multi-View Face Synthesis via Progressive Face Flow"

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2021年 30卷 6700页

作者： Yangyang Xu Xuemiao Xu Jianbo Jiao Keke Li Cheng Xu Shengfeng He School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Subtropical Building Science the Key Laboratory of Big Data and Intelligent Robot Ministry of Education and the Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information South China University of Technology Guangzhou China University of Oxford Oxford U.K.

In the above article [1], unfortunately, Fig. 5 was not displayed correctly with many empty images. The correct version is supplemented here.

关键词： Faces

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Biggy: An implementation of unified framework for big data management system

arXiv

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arXiv 2018年

作者： Wu, Yao Guan, Henan Key Laboratory of Data Engineering Knowledge Engineering of Ministry of Education Beijing China School of Information Renmin University of China Beijing China

Various tools, softwares and systems are proposed and implemented to tackle the challenges in big data on different emphases, e.g., data analysis, data transaction, data query, data storage, data visualization, data privacy. In this paper, we propose datar, a new prospective and unified framework for Big data Management System (BDMS) from the point of system architecture by leveraging ideas from mainstream computer structure. We introduce five key components of datar by reviewing the current status of BDMS. datar features with configuration chain of pluggable engines, automatic dataflow on job pipelines, intelligent self-driving system management and interactive user interfaces. Moreover, we present biggy as an implementation of datar with manipulation details demonstrated by four running examples. Evaluations on efficiency and scalability are carried out to show the performance. Our work argues that the envisioned datar is a feasible solution to the unified framework of BDMS, which can manage big data pluggablly, automatically and intelligently with specific functionalities, where specific functionalities refer to input, storage, computation, control and output of big data. Copyright © 2018, The Authors. All rights reserved.

关键词： Big data

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Automatic knowledge Graph Construction: A Report on the 2019 ICDM/ICBK Contest

Automatic Knowledge Graph Construction: A Report on the 2019...

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IEEE International Conference on data Mining (ICDM)

作者： Xindong Wu Jia Wu Xiaoyi Fu Jiachen Li Peng Zhou Xu Jiang Mininglamp Academy of Sciences Mininglamp Technology Beijing China Key Laboratory of Knowledge Engineering with Big Data (Hefei Univ. of Technology) Ministry of Education Hefei China Department of Computing Macquarie University Sydney Australia School of Computer Science and Technology Anhui University Hefei China

Automatic knowledge graph construction seeks to build a knowledge graph from unstructured text in a specific domain or cross multiple domains, without human intervention. IEEE ICDM 2019 and ICBK 2019 invited teams from both degree-granting institutions and industrial labs to compete in the 2019 knowledge Graph Contest by automatically constructing knowledge graphs in at least two different domains. This article reports the outcomes of the Contest. The participants were expected to build a model to extract knowledge represented as triplets from text data and develop a web application to visualize the triplets. Awards were given to five teams. Their models and key techniques used to construct knowledge graphs are summarized.

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Assessing Mental Stress Based on Smartphone Sensing data: An Empirical Study

Assessing Mental Stress Based on Smartphone Sensing Data: An...

引用

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Feng Wang Yasha Wang Jiangtao Wang Haoyi Xiong Junfeng Zhao Daqing Zhang Key Laboratory of High Confdence Software Technologies Ministry of Education Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Peking University Information Technology Institute Tianjin Binhai China National Engineering Research Center of Software Engineering Beijing China Big Data Lab Baidu Research Beijing

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Mental stress is a critical factor affecting one's physical and mental well-being. At the early stage, the effect of stress is often underestimated, while it usually leads to serious issue Lateran. Therefore, it is crucial to detect stress before it evolves into severe problems. Traditional stress detection methods are based on either questionnaires or professional devices, which are time-consuming, costly and intrusive. With the popularity of smartphones embedded with a rich set of sensors, which can capture people's context, such as movement, sound, location and so on, it is an alternative way to access people's behavior by smartphones. Through an empirical study, this paper proposes an automatic and non-intrusive stress detection framework based on smartphone sensing data. First, we construct various discriminative features from multi-modality phone sensing data, in which both absolute and relative features are considered to make the model more personalized. Then, to tackle the challenge of label insufficiency, we further develop a co-training based method for stress level classification. Finally, we evaluate our model based on an open dataset, and the experimental results verify its advantages over other baselines.

关键词： Sensors Stress Feature extraction Psychology Mobile handsets data models Biomedical monitoring

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On privacy protection of latent dirichlet allocation model training

arXiv

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arXiv 2019年

作者： Zhao, Fangyuan Ren, Xuebin Yang, Shusen Yang, Xinyu School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University China

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the collapsed gibbs sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

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VUSphere: Visual Analysis of Video Utilization in Online Distance education 13

VUSphere: Visual Analysis of Video Utilization in Online Dis...

引用

13th IEEE Conference on Visual Analytics Science and Technology, VAST 2018

作者： He, Huan Zheng, Oinghua Dong, Bo Key Laboratory of Intelligent Networks and Network Security Ministry of Education Xi'An Jiaotong University China School of Electronic and Information Engineering Xi'An Jiaotong University China School of Continuing Education Xi'An Jiaotong University China National Engineering Lab for Big Data Analytics Xi'An Jiaotong University China

ISBN: (纸本)9781538668610

Online Distance education (ODE) provides massive course videos of various specialties for students across the country to learn professional knowledge anytime and anywhere. Analyzing the utilization of these videos from user log data can help academics better understand the learning process of students, evaluate the quality of service provided by regional learning centers, and improve the quality of program curriculum in the future. However, due to the lack of comparable indicators, it is a great challenge to discover the utilization patterns of massive videos and analyze the learning process of large-scale student population from learning log data. In this paper, we introduce a visual analytics system, called VUSphere, to explore the video utilization from multiple perspectives with two proposed indicators. This system offers three coordinated views: A spherical layout overview to depict the overall utilization distribution of videos, courses, and students;a detailed statistics view with four panels to present video utilization statistics of each element from multiple perspectives;and a comparison view to examine the differences in individual elements. Based on the real dataset from our ODE school, several patterns related to video utilization and enrollment are found in the case study with our domain experts. © 2018 IEEE.

关键词： Students

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Polyoxometalate-Cyclodextrin-Based Cluster-Organic Supramolecular Framework for Polysulfide Conversion and Guest–Host Recognition in Lithium-sulfur Batteries

引用

Angewandte Chemie 2023年第36期135卷

作者： Prof. Lubin Ni Jie Gu Xinyuan Jiang Hongjie Xu Zhen Wu Yuchao Wu Yi Liu Prof. Ju Xie Prof. Yongge Wei Prof. Guowang Diao School of Chemistry and Chemical Engineering Yangzhou University Yangzhou 225002 Jiangsu P. R. China Contribution: Conceptualization (lead) Data curation (lead) Funding acquisition (lead) Methodology (lead) Project administration (lead) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Contribution: Data curation (equal) Formal analysis (equal) Investigation (equal) Resources (equal) Software (equal) Contribution: Data curation (equal) Investigation (equal) Software (equal) Contribution: Data curation (equal) Investigation (equal) Software (equal) Validation (equal) Visualization (equal) Contribution: Validation (equal) Visualization (equal) Contribution: Methodology (equal) Visualization (equal) Key Lab of Organic Optoelectronics & Molecular Engineering of Ministry of Education Department of Chemistry Tsinghua University Beijing 100084 P. R. China Contribution: Conceptualization (equal) Project administration (equal) Resources (equal) Supervision (lead) Writing - original draft (equal) Writing - review & editing (lead) Contribution: Resources (lead) Supervision (equal) Writing - review & editing (lead)

Developing polyoxometalate-cyclodextrin cluster-organic supramolecular framework (POM-CD-COSF) still remains challenging due to an extremely difficult task in rationally interconnecting two dissimilar building blocks. Here we report an unprecedented POM-CD-COSF crystalline structure produced through the self-assembly process of a Krebs -type POM, [Zn 2 (WO 2 ) 2 (SbW 9 O 33 ) 2 ] 10− , and two β-CD units. The as-prepared POM-CD-COSF-based battery separator can be applied as a lightweight barrier (approximately 0.3 mg cm −2 ) to mitigate the polysulfide shuttle effect in lithium-sulfur batteries. The designed Li−S batteries equipped with the POM-CD-COSF modified separator exhibit remarkable electrochemical performance, attributed to fast Li + diffusion through the supramolecular channel of β-CD, efficient polysulfide-capture ability by the dynamic host–guest interaction of β-CD, and improved sulfur redox kinetics by the bidirectional catalysis of POM cluster. This research provides a broad perspective for the development of multifunctional supramolecular POM frameworks and their applications in Li−S batteries.

关键词： Cluster-Organic Supramolecular Framework Cyclodextrin Lithium–Sulfur Batteries Polyoxometalates Polysulfide

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Few-shot deep adversarial learning for video-based person re-identification

arXiv

引用

arXiv 2019年

作者： Wu, Lin Wang, Yang Yin, Hongzhi Wang, Meng Shao, Ling Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China University of Queensland St Lucia4072 Australia School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China Abu Dhabi United Arab Emirates

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Thus, matching videos for person re-ID demands flexible models to capture the dynamics in time-series observations and learn view-invariant representations with access to limited labeled training samples. In this paper, we propose a novel few-shot deep learning approach to video-based person re-ID, to learn comparable representations that are discriminative and view-invariant. The proposed method is developed on the variational recurrent neural networks (VRNNs) and trained adversarially to produce latent variables with temporal dependencies that are highly discriminative yet view-invariant in matching persons. Through extensive experiments conducted on three benchmark datasets, we empirically show the capability of our method in creating view-invariant temporal features and state-of-the-art performance achieved by our method. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Highly Stereoselective Diels–Alder Reactions Catalyzed by Diboronate Complexes**

引用

Angewandte Chemie 2023年第33期135卷

作者： Dr. Yuan-He Li Dr. Su-Lei Zhang Dr. Yong Lu Bo Xiao Dr. Tian-Yu Sun Qian-Qian Xu Prof. Dr. Jia-Hua Chen Prof. Dr. Zhen Yang Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education and Beijing National Laboratory for Molecular Science (BNLMS) Peking-Tsinghua Center for Life Sciences Peking University Beijing 100871 China Contribution: Data curation (lead) Investigation (lead) Methodology (lead) Validation (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Methodology (supporting) Writing - review & editing (supporting) Contribution: Data curation (supporting) Writing - review & editing (supporting) Lab of Computational Chemistry and Drug Design State Key Laboratory of Chemical Oncogenomics Peking University Shenzhen Graduate School Shenzhen 518055 China Contribution: Supervision (supporting) Writing - review & editing (supporting) Shenzhen Bay Laboratory Shenzhen 518055 China State Key Laboratory of Chemical Oncogenomics Key Laboratory of Chemical Genomics Peking University Shenzhen Graduate School Shenzhen 518055 China Contribution: Conceptualization (lead) Funding acquisition (lead) Supervision (lead) Writing - review & editing (supporting)

A highly enantioselective catalytic system for exo -Diels–Alder reactions was developed based on the newly discovered bispyrrolidine diboronates (BPDB). Activated by various Lewis or Brønsted acids, BPDB can catalyze highly stereoselective asymmetric exo -Diels–Alder reactions of monocarbonyl-based dienophiles. When 1,2-dicarbonyl-based dienophiles are used, the catalyst can sterically distinguish between the two binding sites, which leads to highly regioselective asymmetric Diels–Alder reactions. BPDB can be prepared as crystalline solids on a large scale and are stable under ambient condition. Single-crystal X-ray analysis of the structure for acid-activated BPDB indicated that its activation involves cleavage of a labile B←N bond.

关键词： Asymmetric Catalysis Bispyrrolidine Diboronates Diels–Alder Reaction Exo-Selectivity Pericyclic Reaction

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Block-Structured Optimization for Anomalous Pattern Detection in Interdependent Networks

Block-Structured Optimization for Anomalous Pattern Detectio...

引用

IEEE International Conference on data Mining (ICDM)

作者： Fei Jie Chunpai Wang Feng Chen Lei Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University at Albany – SUNY Albany NY USA Erik Jonsson School of Engineering & Computer Science The University of Texas at Dallas Dallas TX USA Mininglamp Academy of Sciences Mininglamp Technology Beijing China Institute of Big Knowledge Science Hefei University of Technology Hefei China

We propose a generalized optimization framework for detecting anomalous patterns (subgraphs that are interesting or unexpected) in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others. We frame the problem as a non-convex optimization that has a general nonlinear score function and a set of block-structured and non-convex constraints. We develop an effective, efficient, and parallelizable projection-based algorithm, namely Graph Block-structured Gradient Projection (GBGP), to solve the problem. It is proved that our algorithm 1) runs in nearly-linear time on the network size, and 2) enjoys a theoretical approximation guarantee. Moreover, we demonstrate how our framework can be applied to two very practical applications, and we conduct comprehensive experiments to show the effectiveness and efficiency of our proposed algorithm.

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