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14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Tang, Zhen Yin, Zhixiang Yang, Jing Sun, Xia Cui, Jianzhong School of Mathematics and Big Data AnHui University of Science and Technology Huainan AnHui232001 China School of Electronic and Information Engineering AnHui University of Science and Technology Huainan AnHui232001 China

ISBN: (纸本)9781538680971

DNA strand displacement is a new molecular biological technology with strong operability and simple experimental conditions. In this paper, DNA strand displacement is applied to solve the 0-1 programming problem, and a model is constructed using the class closed-loop DNA structure (known as 'circular DNA' in this paper). The model fixes the DNA strands which represent the variables on the circular DNA single strand in counterclockwise order. The DNA strand 5' end is attached with the primers which are applied to the displacement of the DNA strand. The 3' end is attached with the colored fluorophore. Taking the possible solutions as the input, the results are detected by different fluorescent colors resulting from the DNA strand displacement. The DNA computing model has better flexibility, realizability and applicability. © 2018 IEEE.

关键词： DNA

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Prospect of 5g application in civil airport 1

Prospect of 5g application in civil airport

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1st IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2019

作者： Le, Ningning Zheng, Yinger Huang, Qian Pei, Yanqiu Big Data Analysis and Application Center China Academy of Civil Aviation Science and Technology Beijing China Service Lab Huawei Research Institute Huawei Technology Co. Ltd Beijing China China University of Mining and Technology College of Geoscience and Surveying Engineering Beijing China

ISBN: (纸本)9781728125985

With the rapid growth of China's civil aviation transportation volume, the airport has entered the era of big data in which data is massive, diversified, and converted at high speeds. Achievements for intelligent application of big data based on 5G technology have become an indispensable component of smart airports. Based on the characteristics of 5G technology, this paper combines various state-of-art technologies including Discrete Global Grid Systems, Complex Network, Artificial Intelligence, to prospect the application scenarios of 5G in airport, as efficient and secure wide-area wireless communication system, IoT-based smart monitoring, airport unmanned driving, high-speed entertainment experience, intelligent passenger service. © 2019 IEEE.

关键词： big data

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MCNE: An end-to-end framework for learning multiple conditional network representations of social network

arXiv

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arXiv 2019年

作者： Wang, Hao Xu, Tong Liu, Qi Lian, Defu Chen, Enhong Du, Dongfang Wu, Han Su, Wen Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Tencent Inc China

Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention. Though large efforts have been made, they may fail to describe the multiple aspects of similarity between social users, as only a single vector for one unique aspect has been represented for each node. To that end, in this paper, we propose a novel end-to-end framework named MCNE to learn multiple conditional network representations, so that various preferences for multiple behaviors could be fully captured. Specifically, we first design a binary mask layer to divide the single vector as conditional embeddings for multiple behaviors. Then, we introduce the attention network to model interaction relationship among multiple preferences, and further utilize the adapted message sending and receiving operation of graph neural network, so that multi-aspect preference information from high-order neighbors will be captured. Finally, we utilize Bayesian Personalized Ranking loss function to learn the preference similarity on each behavior, and jointly learn multiple conditional node embeddings via multi-task learning framework. Extensive experiments on public datasets validate that our MCNE framework could significantly outperform several state-of-the-art baselines, and further support the visualization and transfer learning tasks with excellent interpretability and robustness. Copyright © 2019, The Authors. All rights reserved.

关键词： Network embeddings

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CBLNER: A Multi-models Biomedical Named Entity Recognition System Based on Machine Learning 1

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Lejun, Gong Xiaolin, Liu Xuemin, Yang Lipeng, Zhang Yao, Jia Ronggen, Yang Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China

ISBN: (数字)9783030269692

ISBN: (纸本)9783030269685

Biomedical named entities is fundamental recognition task in biomedical text mining. This paper developed a system for identifying biomedical entities with four models including CRF, LSTM, Bi-LSTM and BiLSTM-CRF. The system achieved the following performance in test data Genia V3.02: CRF with an F score of 75.91%, LSTM with an F score of 71.69%, BiLSTM with a F score of 74.37%, BiLSTM-CRF with a F score of 76.81%. Experimental results show the performance of BiLSTM-CRF model is better than other three models. Compared with CRF model, Bi-LSTM-CRF model has better recognition effect for biological entities in long text and entities that modified by modifiers. Therefore, CBLNER system lays a foundation for further relationship and event extraction, and could also provide reference for entity recognition research in other fields. © 2019, Springer Nature Switzerland AG.

关键词： Character recognition

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From local to national metabolism:a review and a scale-up framework

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Ecosystem Health and Sustainability 2020年第1期6卷 334-352,333页

作者： Ningyu Yan Gengyuan Liu Maddalena Ripa Ning Wang Hongmei Zheng Francesco Gonella State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentBeijing Normal UniversityBeijingChina Beijing Engineering Research Center for Watershed Environmental Restoration&Integrated Ecological Regulation BeijingChina Institut De Ciencia I Tecnologia Ambientals Universitat Autonoma De BarcelonaBellaterraSpain Beijing Key Lab of Green Development Decision Based on Big Data Beijing Information Science and Technology UniversityChina Department of Resource Management Tangshan Normal UniversityTangshanChina Department of Molecular Sciences and Nanosystems UniversitàCa’Foscari VeneziaVeniceItaly

Research background:Countries are likely the most important subjects involved in the environmental control and response to global environmental issues,while the majority of the related metabolic analyses are focused on the metabolism at the city ***:Analyzing why and how to scale up the metabolic study from city to ***:Combining quantitative analysis with a literature review socio-metabolic research,since it is an effective method to study resource and environmental issues and has been applied at different ***:1)A single city or a smaller area is hardly self-sufficient,and its sustainability and resilience needs the support of the surrounding environment;2)At the country scale,systems exhibit a higher level of self-organization and a corresponding higher level of complexity,addressing the need for applying the metabolic theory at the national scale;3)The emergy analysis methods show its advantages in study metabolic processes for national metabolism;4)Input-output analysis plays an important role in and region ***:The interactions among and within the scales are nested,as well as the goals and ***,socio-metabolic research on scales differ in their priority goals,and the methods adopted must be targeted.

关键词： National metabolism socio-metabolic research sustainability scale-up

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SkeletonNet: A topology-preserving solution for learning mesh reconstruction of object surfaces from RGB images

arXiv

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arXiv 2020年

作者： Tang, Jiapeng Han, Xiaoguang Tan, Mingkui Tong, Xin Jia, Kui The School of Electronic Information Engineering South China University of Technology The Pazhou Lab Guangzhou China The Chinese University of Hong Kong Shenzhen and Shenzhen Research Institute of Big Data Shenzhen China The School of Software Engineering South China University of Technology Guangzhou China Microsoft Research Asia Beijing China

This paper focuses on the challenging task of learning 3D object surface reconstructions from RGB images. Existing methods achieve varying degrees of success by using different surface representations. However, they all have their own drawbacks, and cannot properly reconstruct the surface shapes of complex topologies, arguably due to a lack of constraints on the topological structures in their learning frameworks. To this end, we propose to learn and use the topology-preserved, skeletal shape representation to assist the downstream task of object surface reconstruction from RGB images. Technically, we propose the novel SkeletonNet design that learns a volumetric representation of a skeleton via a bridged learning of a skeletal point set, where we use parallel decoders each responsible for the learning of points on 1D skeletal curves and 2D skeletal sheets, as well as an efficient module of globally guided subvolume synthesis for a refined, high-resolution skeletal volume;we present a differentiable Point2Voxel layer to make SkeletonNet end-to-end and trainable. With the learned skeletal volumes, we propose two models, the Skeleton-Based Graph Convolutional Neural Network (SkeGCNN) and the Skeleton-Regularized Deep Implicit Surface Network (SkeDISN), which respectively build upon and improve over the existing frameworks of explicit mesh deformation and implicit field learning for the downstream surface reconstruction task. We conduct thorough experiments that verify the efficacy of our proposed SkeletonNet. SkeGCNN and SkeDISN outperform existing methods as well, and they have their own merits when measured by different metrics. Additional results in generalized task settings further demonstrate the usefulness of our proposed methods. We have made our implementation code publicly available at https://***/tangjiapeng/SkeletonNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Musculoskeletal system

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Improved performance of wireless sensor network based on fuzzy logic for clustering scheme 2nd

Improved performance of wireless sensor network based on fuz...

引用

2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Nguyen, Trong-The Pan, Jeng-Shyang Chu, Shu-Chuan Dao, Thi-Kien Do, Van-Chieu Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology FuzhouFujian China College of Science and Engineering Flinders University TonsleySA5042 Australia Department of Information Technology Hai-Phong Private University Hai Phong Viet Nam

ISBN: (纸本)9783030045845

The wireless sensor network (WSN) consists of a large number of sensor nodes collaborative to collect and transmit data to the end user. Since the network’s long life is an utmost requirement of WSN. Clustering is one of the most effective ways of prolonging the lifetime of the network. In clustering, a node takes charge of the cluster to coordinate and receive information from the member nodes and transfer it to the sink. With the imbalance of energy dissipation by the sensor node, it may lead to premature failure of the network. Therefore, a robust balanced clustering algorithm can solve this issue in which a worthy candidate will play the cluster head role in each round. This paper proposes an improvement of WSN based on fuzzy logic for clustering. Residual energy, distance from the sink, and density of the nodes in its locality are taken account as the input to feed into fuzzy inference system. Compared results with the other approaches in the literature show the proposed scheme provides the better performance in terms of stability period and protracted lifetime. © Springer Nature Switzerland AG 2019.

关键词： Energy efficiency

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DNA Origami Based Computing Model for the Satisfiability Problem 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Yang, Zhenqin Yin, Zhixiang Cui, Jianzhong Yang, Jing School of Mathematics and Big Data Anhui University of Science and Technology Huainan China School of Electronic and Information Engineering Anhui University of Science and Technology Huainan232001 China

ISBN: (数字)9789811328268

ISBN: (纸本)9789811328251

The satisfiability problem (SAT) is one of the NP-complete problems in the fields of theoretical computer and artificial intelligence, is the core of NP-complete problems. Compared with traditional DNA self-assembly, DNA origami is a new method of DNA self-assembly. We first give a description and the status quo of study of the satisfiability problem, briefly introduce the principle of DNA origami, propose the computing model based on DNA origami to solve the satisfiability problem, and solve an instance with 3 variables, 3 clauses to illustrate the feasibility of the algorithm. The proposed model only uses gel electrophoresis to search the solution to the problem, which is the most reliable biological operation known to date, therefore the proposed model is feasible. At present, the reported results concerning using origami to solve the NP-complete problem is relatively few. Our method is a new attempt to solve the NP- complete problem using biological DNA molecules. © 2018, Springer Nature Singapore Pte Ltd.

关键词： DNA

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

引用

23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation

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IFAC-PapersOnLine 2020年第2期53卷 15029-15034页

作者： S.Y. Liu S. Lin Y.B. Wang B. De Schutter W.H.K. Lam School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100089 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences College of Civil Engineering and Architecture Zhejiang University PR China Delft Center for Systems and Control Delft University of Technology The Netherlands Department of Civil and Structural Engineering The Hong Kong Polytechnic University Hong Kong SAR China

In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks.

关键词： Markov traffic model Traffic signals Bayesian Urban traffic network

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