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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yu Wang Hui Wang Meijie Hou Yaojun Wang Dongbo Bu Chunming Zhang Chuncui Huang Shiwei Sun Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 6 Kexueyuan South Road Beijing China 19 Yuquan Road University of Chinese Academy of Sciences Beijing China College of Information and Electrical Engineering China Agricultural University 17 Qinghua Donglu Beijing China Zhongke Big Data Academy Zhengzhou Henan China Phil Rivers Technology Beijing China 15 Datun Road Institute of Biophysics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665429825

Glycans play important roles in a great variety of biological processes, and these roles are closely determined by the details of their structures. It becomes possible to acquire hidden features from glycan structures using deep learning method with the great progress in recent years. Unlike the linear chain of proteins and DNAs, branching is a unique feature of glycan structures, which makes it very difficult to directly apply deep learning models on glycans. Thus, how to comprehensively and efficiently describe glycans and use them as input to deep learning models still remains challenging. Here, a graph neural network (GNN) called GlyNet was used to obtain high-dimensional representation of glycans and predict their immunogenicity. Our method was applied in SugarBase, and it works more superiorly than the state-of-art method with accuracy increased from 91.7% to 95.6%.

关键词： Deep learning Proteins Spectroscopy databases Conferences Biological processes Ions

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OBSCURE: Information-theoretic oblivious and verifiable aggregation queries 45th

OBSCURE: Information-theoretic oblivious and verifiable aggr...

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45th International Conference on Very Large data Bases, VLDB 2019

作者： Gupta, Peeyush Li, Yin Mehrotra, Sharad Panwar, Nisha Sharma, Shantanu Almanee, Sumaya University of California Irvine United States Xinyang Normal University and Henan Key Lab of Analysis and Application of Educating Big Data China

Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. We develop communication-efficient and information-theoretically secure algorithms for privacy-preserving aggregation queries using multi-party computation (MPC). Specifically, query processing techniques over secret-shared data outsourced by single or multiple database owners are developed. These algorithms allow a user to execute queries on the secret-shared database and also prevent the network and the (adversarial) clouds to learn the user's queries, results, or the database. We further develop (non-mandatory) privacypreserving result verification algorithms that detect malicious behaviors, and experimentally validate the efficiency of our approach over large datasets, the size of which prior approaches to secretsharing or MPC systems have not scaled to. © 2018, VLDB Endowment.

关键词： Query processing

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Prediction model of physical activity level and hypertension based on artificial neural network

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Journal of Physics: Conference Series 2021年第1期1848卷

作者： Liang Hua Wang Yu Li Jinying Zhang Wanjun Wang Yiting school of Physical Education Henan University Kaifeng Henan 475001 China Department of Health Management People's Hospital of Henan Province People's Hospital of Henan University People's Hospital of Zhengzhou University Zhengzhou 450003 China Key Laboratory of Big Data Analysis and Processing of Henan province Henan University Kaifeng 475004 PR China school of Foreign Language Henan University Kaifeng Henan 475001 China

Purpose: Taking the population of henan Provincial People's Hospital Chronic Disease Control Center from July 1, 2020 to December 31, 2020 who came to the hospital for physical examination as the research object, analyze and explore the role and contribution of physical activity level in the prediction of hypertension by using the LSTM network model, which can provide references for the clinical diagnosis of hypertension. Methods: Randomly select 2000 physical examination data, remove missing and invalid data, and preprocess them. Finally, select factors such as gender, age, body mass index, weight grade, waist-to-hip ratio, physical activity level, body fat percentage and other factors to establish a neural network prediction model. Then test and study the model, focusing on exploring the contribution of physical activity level to prediction. Result: The level of physical activity has certain advantages in predicting the prevalence of hypertension, but the predictive ability in the later stage is insufficient

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Comparison of Satellite-retrieved NO2 Vertical Column Density with Ground-level NO2 concentration in a provincial scale region

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IOP Conference Series: Earth and Environmental Science 2020年第1期615卷

作者： Fengbin Zheng Yu Xia Qiang Ge Kun Cai Henan Engineering Laboratory of Spatial Information Processing Henan University Henan 475004 PR China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan 475004 PR China School of Computer and Information Engineering Henan University Henan 475004 PR China

Satellite remote sensing will have certain errors in data retrieval accuracy, which requires further ground observation verification. This paper examines and verifies 2019 Jiangsu provincial Ground-level monitoring data and TROPOMI data to evaluate the applicability of TROPOMI tropospheric NO2 Vertical Column Density (VCD) data and compares the results to OMI data analysis verification results. The results show that the correlation coefficient r between the TROPOMI NO2 data and Ground-level monitoring data at the monthly mean scale is as high as 0.9, and consistent seasonal cyclic changes are observed. The correlation between Ground-level monitoring data and OMI NO2 data is lower than TROPOMI (r=0.78 < 0.9). Compared to OMI satellites, the TROPOMI attains a smaller deviation in tropospheric NO2 monitoring. Jiangsu Province had the highest monthly average concentration in January, which were 38.67 ug/m3 (Ground-level), 17.35 × 1015 molec/cm2 (TROPOMI) and 20.04 × 1015 molec/cm2 (OMI). The lowest concentration in August was 13.39 ug/m3 (Ground-level), 5.2 × 1015 molec/cm2 (TROPOMI) and 7.03 × 1015 molec/cm2 (OMI).

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Varied Priority-Based data Forwarding via NDN in VANET

Varied Priority-Based Data Forwarding via NDN in VANET

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

作者： Siyang Wang Weigang Wu Zhou Zhi School of Data and Computer Science Sun Yet-Sen University Guangzhou China Guangdong Province Key Lab. of Big Data Analysis and Processing Sun Yet-Sen University Guangzhou China Sun Yet-Sen University Guangzhou China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Recently, Named data Networking (NDN) has been used for vehicular networks to improve the efficiency of data forwarding. However, existing solutions for vehicular NDN (V-NDN) rarely consider the priority of data packets. In this paper, we propose a priority-based data packet forwarding algorithm. We observed that different data types may have different priorities and the importance of a data packet decreases as the distance between data packet location and data source location increases. Our algorithm handles data packets differently according to their priorities. It first assigns data packets into three priority levels: high, medium and low. Then, when transmitting a data packet, the node uses either push or pull-based methods depending on the data packet's priority. For high-priority data packets, the push-based forwarding algorithm is adopted; for medium-and low-priority data packets, the pull-based forwarding algorithm is employed. With our design, the push-based forwarding algorithm is used in the local area to improve the hit ratio of high priority data; the pull-based forwarding algorithm is used to disseminate data packets to interested nodes and to reduce network overhead. Our algorithm satisfies the vehicle's need for different priority. Compared with existing works, our algorithm has a higher hit ratio than pure pull-based NDN and costs less overhead than traditional IP architectures. Simulations via SUMO, OpenStreetMap and ndnSIM verify the advantages of our work.

关键词： Roads Vehicular ad hoc networks Wireless communication Sun IP networks Routing protocols

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Early Diagnosis of Alzheimer’s Disease Based on Deep Learning and GWAS 1st

Early Diagnosis of Alzheimer’s Disease Based on Deep Learni...

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1st International Workshop on Human Brain and Artificial Intelligence, HBAI 2019, held in conjunction with the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019

作者： Pan, Dan Huang, Yin Zeng, An Jia, Longfei Song, Xiaowei Guangdong Construction Polytechnic Guangzhou510440 China Guangzhou Dazhi Networks Technology Co. Ltd. Guangzhou510000 China Guangdong University of Technology Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China ImageTech Lab Simon Fraser University VancouverV6B 5K3 Canada

ISBN: (纸本)9789811513978

Alzheimer’s disease (AD) is a typical irreversible neurodegenerative disease. At present, the pathogenesis of AD remains elusive and the effective treatment of AD is still a challenge for clinicians. Therefore, early diagnosis is of great importance for the development of new drugs to prevent the progression of AD. With the rapid advancement of neuroimaging technology and deep learning, more and more researchers have turned to deep learning to analyze the brain images for early diagnosis of AD. Plus, studies have demonstrated that it is very likely that the genetic makeup of an individual may influence his/her susceptibility to AD traits. Researchers have begun to identify the genetic biomarkers associated to AD and evaluate the effects of genes upon the changes in the structure and function of the brain of AD patients. In this study, an ensemble model of multi-slice classifiers based on convolutional neural network (CNN) was proposed to make an early diagnosis of AD and at the same time to identify the significant brain regions related to AD. The morphological data of these identified brain regions and the genotype were utilized to carry out genome-wide association studies (GWAS) to explore the potential genetic biomarkers of AD. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On

FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On

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International Conference on Computer Vision (ICCV)

作者： Haoye Dong Xiaodan Liang Xiaohui Shen Bowen Wu Bing-Cheng Chen 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 ByteDance AI Lab

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Beyond current image-based virtual try-on systems that have attracted increasing attention, we move a step forward to developing a video virtual try-on system that precisely transfers clothes onto the person and generates visually realistic videos conditioned on arbitrary poses. Besides the challenges in image-based virtual try-on (e.g., clothes fidelity, image synthesis), video virtual try-on further requires spatiotemporal consistency. Directly adopting existing image-based approaches often fails to generate coherent video with natural and realistic textures. In this work, we propose Flow-navigated Warping Generative Adversarial Network (FW-GAN), a novel framework that learns to synthesize the video of virtual try-on based on a person image, the desired clothes image, and a series of target poses. FW-GAN aims to synthesize the coherent and natural video while manipulating the pose and clothes. It consists of: (i) a flow-guided fusion module that warps the past frames to assist synthesis, which is also adopted in the discriminator to help enhance the coherence and quality of the synthesized video; (ii) a warping net that is designed to warp clothes image for the refinement of clothes textures; (iii) a parsing constraint loss that alleviates the problem caused by the misalignment of segmentation maps from images with different poses and various clothes. Experiments on our newly collected dataset show that FW-GAN can synthesize high-quality video of virtual try-on and significantly outperforms other methods both qualitatively and quantitatively.

关键词： Integrated circuits Generators Gallium nitride Three-dimensional displays Image generation Computational modeling Visualization

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A novel cloud game framework in dual Gigabit Network

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Journal of Physics: Conference Series 2020年第1期1684卷

作者： Kai Sun Tao Xu Shanghai Urban Construction Vocational College Shanghai 201415 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng 475004 China. School of Computer and Information Engineering Henan University Kaifeng 475004 China.

With the development of 5G, cloud computing, and artificial intelligence (AI) technology, cloud game has become a hotspot about application and innovation of Gigabit bandwidth. This paper, firstly, analyses the present situation and development of cloud game to clarify the key business and key technology of cloud game, then, proposes an operational framework of cloud game, finally, the feasibility of the proposed framework is discussed and several suggestions about the operation of cloud game are given.

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Identifying High Potential Talent: A Neural Network Based Dynamic Social Profiling Approach

Identifying High Potential Talent: A Neural Network Based Dy...

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IEEE International Conference on data Mining (ICDM)

作者： Yuyang Ye Hengshu Zhu Tong Xu Fuzhen Zhuang Runlong Yu Hui Xiong Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology University of Chinese Academy of Sciences

How to identify high-potential talent (HIPO) earlier in their career always has strategic importance for human resource management. While tremendous efforts have been made in this direction, most existing approaches are still based on the subjective selection of human resource experts. This could lead to unintentional bias and inconsistencies. To this end, in this paper, we propose a neural network based dynamic social profiling approach for quantitatively identifying HIPOs from the newly-enrolled employees by modeling the dynamics of their behaviors in organizational social networks. A basic assumption is that HIPOs usually perform more actively and have higher competencies than their peers to accumulate their social capitals during their daily work practice. Along this line, we first propose to model the social profiles of employees with both Graph Convolutional Network (GCN) and social centrality analysis in a comprehensive way. Then, an adaptive Long Short Term Memory (LSTM) network with global attention mechanism is designed to capture the profile dynamics of employees in the organizational social networks during their early career. Finally, extensive experiments on real-world data clearly validate the effectiveness of our approach as well as the interpretability of our results.

关键词： Social networking (online) Companies Artificial neural networks Germanium Engineering profession

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Towards Multi-Pose Guided Virtual Try-On Network

Towards Multi-Pose Guided Virtual Try-On Network

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International Conference on Computer Vision (ICCV)

作者： Haoye Dong Xiaodan Liang Xiaohui Shen Bochao Wang Hanjiang Lai Jia Zhu Zhiting Hu 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 ByteDance AI Lab. School of Computer Science South China Normal University Guangzhou Key Laboratory of Big Data and Intelligent Education Carnegie Mellon University

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Virtual try-on systems under arbitrary human poses have significant application potential, yet also raise extensive challenges, such as self-occlusions, heavy misalignment among different poses, and complex clothes textures. Existing virtual try-on methods can only transfer clothes given a fixed human pose, and still show unsatisfactory performances, often failing to preserve person identity or texture details, and with limited pose diversity. This paper makes the first attempt towards a multi-pose guided virtual try-on system, which enables clothes to transfer onto a person with diverse poses. Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-On Network (MG-VTON) generates a new person image after fitting the desired clothes into the person and manipulating the pose. MG-VTON is constructed with three stages: 1) a conditional human parsing network is proposed that matches both the desired pose and the desired clothes shape; 2) a deep Warping Generative Adversarial Network (Warp-GAN) that warps the desired clothes appearance into the synthesized human parsing map and alleviates the misalignment problem between the input human pose and the desired one; 3) a refinement render network recovers the texture details of clothes and removes artifacts, based on multi-pose composition masks. Extensive experiments on commonly-used datasets and our newly-collected largest virtual try-on benchmark demonstrate that our MG-VTON significantly outperforms all state-of-the-art methods both qualitatively and quantitatively, showing promising virtual try-on performances.

关键词： Shape Three-dimensional displays Generators Hair Task analysis Face Gallium nitride

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