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15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018

作者： Chen, Jia Xu, Tongge Zhang, Xiong State Key Laboratory of Software Development Environmentneering Beihang University Beijing100191 China Lab 673 School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781538650530

Social relationship recommenders aim at predicting potential useful relationships with high accuracy and efficiency, which is critically important in social network services for addressing information overload. Existing relationship recommenders mostly emphasize on friend recommendation in online social networks, which can not satisfy the requirements of industrial information systems. This work proposes an efficient latent-factor (LF)-based approach to predict multicategory relationships rather than only friendship in a social network. The main idea is to construct multicategory relationship data and develop the corresponding recommenders. To do so, two dimensions are designed for social relationship data, i.e., a category dimension built on real social relationship types, and an extended dimension built on the involved persons. Depending on the two dimensional relationship data, we construct a rating matrix by analyzing user preferences to each social relationship category one belonged to. For analyzing the resultant rating matrix with high accuracy, the hill-climbing and extended-linear-biases-enhanced latent factor (HC-ELBLF) model is proposed. The original grid-search-based learning process in the original ELBLF is substituted by the hill-climbing algorithm, which is an efficient and practical greed algorithm for parameter selection. The experimental results on two industrial datasets show effectiveness of the proposed HC-ELBLF approach. © 2018 IEEE.

关键词： Matrix algebra

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Residual Networks for Light Field Image Super-Resolution

Residual Networks for Light Field Image Super-Resolution

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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Shuo Zhang Youfang Lin Hao Sheng Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.

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Dynamic Scheduling Policy for Patient Flow in a Smart environment

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Chinese Journal of Electronics 2017年第3期26卷 530-536页

作者： CHEN Xiao DING Jie Nigel Thomas School of Computer Science and Communication Engineering Jiangsu University State Key Laboratory of Software Development Environment Beihang University School of Information Engineering Yangzhou University School of Computing Science Newcastle University

Given the popularity of smart environment in a hospital involving the participation of a large group of patients,scheduling of patients towards their desired destination is still being a challenging *** the development of Internet of Things(Io T),smart environments are increasingly being deployed in various public scenarios and in particular,hospitals are an important target environment for smart *** paper models a dynamic scheduling policy based on a patient flow scenario in order to solve the queuing issue and facilitate people’s *** dynamic scheduling policy aims to forward incoming patients to dissolve jams especially at the peaks by giving accurate predication of appointment *** main modelling technique is a formal method—Performance evaluation process algebra(PEPA).The findings show that the dynamic scheduling policy is able to efficiently improve the patient flow.

关键词： Dynamic scheduling Patient flow Smart environment Formal method Performance evaluation process algebra(PEPA)

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Impact of temperature and relative humidity on the transmission of COVID-19: A modeling study in China and the United states

arXiv

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arXiv 2020年

作者： Wang, Jingyuan Tang, Ke Feng, Kai Lin, Xin Lv, Weifeng Chen, Kun Wang, Fei School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Social Sciences Tsinghua University China State Key Laboratory of Software Development Environment Beihang University China Department of Statistics University of Connecticut United States Center for Population Health University of Connecticut Health Center United States Department of Population Health Sciences Weill Cornell Medical College Cornell University United States

Objectives We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, and human mobility status. Design A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. Setting We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1,005 U.S. counties. Participants A total of 69,498 cases in China and 740,843 cases in the U.S. are used for calculating the effective reproductive numbers. Primary outcome measures Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). Results Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the U.S. Conclusions Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1 degree Celsius is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI [-0.0395,-0.0125]) in China and by 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.;an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI [-0.0108,-0.0045]) in China and by 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor

arXiv

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arXiv 2019年

作者： Li, Mingzhen Liu, Changxi Liao, Jianjin Zheng, Xuegui Yang, Hailong Sun, Rujun Xu, Jun Gan, Lin Yang, Guangwen Luan, Zhongzhi Qian, Depei School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Beijing China National University of Singapore Singapore Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM achieves 1.79× on average compared to the state-of-the-art deep learning framework on Sunway, across six representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor. Copyright © 2019, The Authors. All rights reserved.

关键词： Program compilers

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A probabilistic framework of preference discovery from folksonomy corpus

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Frontiers of computer science 2017年第6期11卷 1075-1084页

作者： Xiaohui GUO Chunming HU Richong ZHANG Jinpeng HUAI State Key Lab of Software Development Environment Beihang University Beijing 100191 China Institute of Advanced Computing Technology School of Computer Science and Engineering Beihang University Beijing 100191 China

The increasing availab.lity of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or re- trieval results. This paper presents a rigorous probabilis- tic framework to discover user preference from folkson- omy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applica- bility of the engaged models. The experimental results show that, with the help of the proposed framework, the user pref- erence can be effectively discovered.

关键词： preference discovery tagging folksonomy so-cial annotation

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A Tetra-hex Hybrid Mesh Generation Method Based on Delaunay Triangulation

A Tetra-hex Hybrid Mesh Generation Method Based on Delaunay ...

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2018 International Conference on Mathematics, Modelling, Simulation and Algorithms（MMSA2018）

作者： Pengfei Zhan Xianhai Meng Zhongxiang Duan Qin Yang School of Computer Science and Engineering Beihang University (BUAA) State Key Laboratory of Software Development Environment

ISBN: (纸本)9781510863064

This paper presents an automatic method to generate a hex-dominant hybrid mesh with the input being a Delaunay tetrahedral mesh. In this method, we replace the interior tetrahedra in the original mesh with regular hexahedra, and fill the cavity between the boundary tetrahedral and the interior structured elements with tetrahedral elements. The mesh generated by this method is a tetra-hex hybrid mesh which only consists of two types of grids. The result demonstrated that our method can perform on most tetrahedral mesh successfully. The total number of grids in the hybrid mesh is reduced markedly, and moreover, the resultant mesh can keep the conformity of the boundary and consist of grids with high quality.

关键词： hybrid mesh advancing front technique delaunay triangulation

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Real-Time Rendering of Massive Geological Data

Real-Time Rendering of Massive Geological Data

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2018 International Conference on Mathematics, Modelling, Simulation and Algorithms（MMSA2018）

作者： Zhijie Jia Xianhai Meng Zhongxiang Duan Qin Yang School of Computer Science and Engineering Beihang University (BUAA) State Key Laboratory of Software Development Environment

ISBN: (纸本)9781510863064

The visualization technique is an effective tool to extract useful information from the geological data. In this paper, we proposed an efficient visualization framework that can render large-scale geological truncated mesh in real-time. The geological models of different resolutions were generated by cell merging, and the rendering resolution was dynamically selected according to the viewpoint position. The experiments confirm the ability to handle massive geological data of our method, and the coarse resolution rendering only causes little detail loss for the visual image.

关键词： geological data LOD real-time rendering

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Budget-Aware Dynamic Incentive Mechanism in SpatialCrowdsourcing

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Journal of computer science & Technology 2017年第5期32卷 890-904页

作者： Jia-Xu Liu Yu-Dian Ji Wei-Feng Lv Ke Xu State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China School of Software Liaoning Technical University Huludao 125105 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China

The ubiquitous deployment of GPS-equipped devices and mobile networks has spurred the popularity of spatial crowdsourcing. Many spatial crowdsourcing tasks require crowd workers to collect data from different locations. Since workers tend to select locations nearby or align to their routines, data collected by workers are usually unevenly distributed across the region. To encourage workers to choose remote locations so as to avoid imbalanced data collection, we investigate the incentive mechanisms in spatial crowdsourcing. We propose a price adjustment function and two algorithms, namely DFBA (Dynamic Fixed Budget Allocation) and DABA (Dynamic Adjusted Budget Allocation), which utilize price leverage to mitigate the imbalanced data collection problem. Extensive evaluations on both synthetic and real-world datasets demonstrate that the proposed incentive mechanisms are able to effectively balance the popularity of different locations.

关键词： incentive mechanism crowdsensing spatial crowdsourcing

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ADCMO: An Anomaly Detection Approach Based on Local Outlier Factor for Continuously Monitored Object

ADCMO: An Anomaly Detection Approach Based on Local Outlier ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Shubin Su Limin Xiao Li Ruan Rongbin Xu Shupan Li Zhaokai Wang Qigong He Wei Li State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Classified Information Carrier Safety Management Engineering Technology Research Center of Beijing Beijing Jinghang Computation and Communication Research Institute Beijing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Since the existing works in data stream anomaly detection mainly locate abnormal objects from data sequences instead of detecting the abnormal state of a monitored object in real-time. And they need a set of empirical predefined thresholds, which may affect the flexibility of the detection models. These limitations make the existing solutions no longer suitable for the real-time detection of the changes in the status of the monitored object. In this paper, we propose a new Abnormal conditions Detection approach (ADCMO) on the Continuously Monitored Object. ADCMO incorporates the data distribution property to carry out the anomaly quantification for the data in the data stream, and then the abnormal conditions of the monitored object are quantified by the anomaly information of the current data and historical data. Finally, the anomaly state is adaptively determined by the distribution of anomaly coefficients. ADCMO can detect the changes in the status by calculating the outlier coefficient of the object at each moment and adaptively make the abnormal early warning. The experiments show that ADCMO can do this any-time, dynamically, efficiently and effectively.

关键词： Monitoring Biomedical monitoring Anomaly detection Real-time systems Transient analysis lab.ratories software

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