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19th IEEE International Conference on Communication technology, ICCT 2019

作者： Zhang, Kun Chen, Jing Shen, Chong Chen, Yu Long, Keliu Aslam, Bhatti Uzair Education Center of MTA Hainan Tropical Ocean University Sanya Hainan572022 China State Key Laboratory of Marine Resources Utilization in South China Sea Hainan University Haikou Hainan570228 China College of Computer Science and Technology Hainan Tropical Ocean University Sanya Hainan572022 China College of Computer Science and Information Technology Nanjing Normal University Nanjing Jiangsu210023 China

ISBN: (纸本)9781728105352

How to reduce the energy consumption of the network and extend the life cycle of the wireless sensor network is one of the hotspots in the research of wireless sensor network. In the premise of ensuring the coverage of network, the node scheduling algorithm that let redundant nodes turn into the sleep state is a very effective energy-saving measure. In this paper, a node scheduling mechanism based on grid is proposed. The mechanism calculates the weight of each node in each grid, and then determine whether the node is a redundant coverage node. The result of Matlab simulation shows that the mechanism can effectively prolong the survival time of network. © 2019 IEEE.

关键词： Scheduling algorithms

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An Integrated and Intelligent Dental Healthcare System with Mobile Services 15th

An Integrated and Intelligent Dental Healthcare System with ...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Xu, Yueshen Lu, Yi Li, Rui Niu, Lin Du, Wenzhi An, Ni Liu, Yaning Liu, Xinyi Jiang, Yan Li, Zhenhua Guo, Jin Wang, Xiangdong School of Computer Science and Technology Xidian University Xi’an710071 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China Stomatological Hospital Xi’an Jiaotong University Xi’an710004 China Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Diseases College of Stomatology Xi’an Jiaotong University Xi’an China Shaanxi Lishengruihui Technology Co. Ltd. Xi’an710075 China

ISBN: (纸本)9783030301453

Medical informatization takes a key role in medical and healthcare industries, which is a necessary way of improving service quality and treatment experience in a hospital. In this paper, we design and implement an integrated and intelligent healthcare system with mobile services, specific to dental healthcare. The developed system contains four components, including WeChat official account, intelligent question and answering (Q&A) system, mobile follow-up care mini program and AI speech assistant. The developed WeChat official account and intelligent Q&A system provide a large amount of professional knowledge on dental health, which can help narrow the knowledge gap between doctors and patients. The two components facilitate patients to follow up our applications without downloading any extra softwares, as the developed functions (e.g., voice service) are provided through mobile services. These two components also facilitate follow-up management, decreasing manpower and resource usage in hospital. Our system is developed to serve patients as well as dentists, and provides a group of interactive healthcare services in a low cost. In this paper, we elaborate the whole system architecture and implementation detail of each component. We also report the performance test result and training process of the classifier. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Hospitals

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STR: Secure computation on additive shares using the share-transform-reveal strategy

arXiv

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arXiv 2020年

作者： Xia, Zhihua Gu, Qi Zhou, Wenhao Xiong, Lizhi Weng, Jian Xiong, Naixue N. College of Cyber Security Jinan University Guangzhou510632 China Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Jiangsu Engineering Center of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology Nanjing210044 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China The College of Intelligence and Computing Tianjin University Tianjin300350 China

The rapid development of cloud computing probably benefits many of us while the privacy risks brought by semi-honest cloud servers have aroused the attention of more and more people and legislatures. In the last two decades, plenty of works seek to outsource various specific tasks to servers while ensuring the security of private data. The tasks to be outsourced are countless;however, the computations involved are similar. In this paper, we construct a series of novel protocols that support the secure computation of various functions on numbers (e.g., the basic elementary functions) and matrices (e.g., the calculation of eigenvectors and eigenvalues) on arbitrary n ≥ 2 servers. All protocols only require constant rounds of interactions and achieve low computation complexity. Moreover, the proposed n-party protocols ensure the security of private data even though n − 1 servers collude. The convolutional neural network models are utilized as the case studies to verify the protocols. The theoretical analysis and experimental results demonstrate the correctness, efficiency, and security of the proposed protocols. Copyright © 2020, The Authors. All rights reserved.

关键词： Additives

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Unsupervised ensemble learning based on graph embedding for image clustering 25th

Unsupervised ensemble learning based on graph embedding for ...

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25th International Conference on Neural information processing, ICONIP 2018

作者： Luo, Xiaohui Zhang, Li Li, Fanzhang Hu, Chengxiang School of Computer Science and Technology and Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (纸本)9783030041816

Manifold learning has attracted more and more attention in machine learning for past decades. Unsupervised Large Graph Embedding (ULGE), which performs well on the large-scale data, has been proposed for manifold learning. To improve the clustering performance, a novel Unsupervised Ensemble Learning based on Graph Embedding (UEL-GE) is explored, which takes ULGE to get low-dimensional embeddings of the given data and uses the K-means method to obtain the clustering results. Furthermore, the multiple clusterings are corrected by using the bestMap method. Finally, the corrected clusterings are combined to generate the final clustering. Extensive experiments on several data sets are conducted to show the efficiency and effectiveness of the proposed ensemble learning method. © Springer Nature Switzerland AG 2018.

关键词： K-means clustering

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Ontology Summarization: Graph-Based Methods and Beyond

Ontology Summarization: Graph-Based Methods and Beyond

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作者： Pouriyeh, Seyedamin Allahyari, Mehdi Liu, Qingxia Cheng, Gong Arabnia, Hamid Reza Atzori, Maurizio Mohammadi, Farid Ghareh Kochut, Krys Department of Information Technology Kennesaw State University MariettaGA30060 United States Department of Computer Science Georgia Southern University StatesboroGA30460 United States National Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Department of Computer Science University of Georgia AthensGA30602 United States Department of Mathematics and Computer Science University of Cagliari Palazzo delle Scienze Via Ospedale 72 Cagliari09124 Italy

Ontologies have been widely used in numerous and varied applications, e.g. to support data modeling, information integration, and knowledge management. With the increasing size of ontologies, ontology understanding, which is playing an important role in different tasks, is becoming more difficult. Consequently, ontology summarization, as a way to distill key information from an ontology and generate an abridged version to facilitate a better understanding, is getting growing attention. In this survey paper we review existing ontology summarization techniques and focus mainly on graph-based methods, which represent an ontology as a graph and apply centrality-based and other measures to identify the most important elements of an ontology as its summary. After analyzing their strengths and weaknesses, we highlight a few potential directions for future research. © 2019 World Scientific Publishing Company.

关键词： Ontology

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Positive-Unlabeled Learning for Sentiment Analysis with Adversarial Training 15th

Positive-Unlabeled Learning for Sentiment Analysis with Adve...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Xu, Yueshen Li, Lei Huang, Jianbin Yin, Yuyu Shao, Wei Mai, Zhida Hei, Lei School of Computer Science and Technology Xidian University Xi’an710071 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China School of Computer Hangzhou Dianzi University Hangzhou310018 China School of Science RMIT University MelbourneVIC3001 Australia Xanten Guangdong Development Co. Ltd. Foshan528200 China Center of Journal Publication Xidian University Xi’anShaanxi710071 China

ISBN: (纸本)9783030301453

Sentiment classification is a critical task in sentiment analysis and other text mining applications. As a sub-problem of sentiment classification, positive and unlabeled learning or positive-unlabeled learning (PU learning) problem widely exists in real-world cases, but it has not been given enough attention. In this paper, we aim to solve PU learning problem under the framework of adversarial training and neural network. We propose a novel model for PU learning problem, which is based on adversarial training and attention-based long short-term memory (LSTM) network. In our model, we design a new adversarial training technique. We conducted extensive experiments on two real-world datasets. The experimental results demonstrate that our proposed model outperforms the compared methods, including the well-known traditional methods and state-of-the-art methods. We also report the training time, and discuss the sensitivity of our model to parameters. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Long short-term memory

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Integrating Bi-Dynamic Routing Capsule Network with Label-Constraint for Text classification

Integrating Bi-Dynamic Routing Capsule Network with Label-Co...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Xiang Guo Youquan Wang Kaiyuan Gao Jie Cao Haicheng Tao Chaoyue Chen School of Information Engineering Nanjing University of Finance and Economics Nanjing China Jiangsu Provincial Key Laboratory of E-Business Nanjing University of Finance and Economics Nanjing China Department of Mathematical Sciences Xi’an Jiaotong-Liverpool University Suzhou China College of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Neural-based text classification methods have attracted increasing attention in recent years. Unlike the standard text classification methods, neural-based text classification methods perform the representation operation and end-to-end learning on the text data. Many useful insights can be derived from neural based text classifiers as demonstrated by an ever-growing body of work focused on text mining. However, in the real-world, text can be both complex and noisy which can pose a problem for effective text classification. An effective way to deal with this issue is to incorporate self-attention and capsule networks into text mining solutions. In this paper, we propose a Bi-dynamic routing Capsule Network with Label-constraint (BCNL) model for text classification, which moves beyond the limitations of previous methods by automatically learning the task-relevant and label-relevant words of text. Specifically, we use a Bi-LSTM and self-attention with position encoder network to learn text embeddings. Meanwhile, we propose a bi-dynamic routing capsule network with label-constraint to adjust the category distribute of text capsules. Through extensive experiments on four datasets, we observe that our method outperforms state-of-the-art baseline methods.

关键词： Routing Semantics Feature extraction Heuristic algorithms Task analysis Finance Economics

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Computation Offloading and Resource Allocation for MEC in C-RAN: A Deep Reinforcement Learning Approach 19

Computation Offloading and Resource Allocation for MEC in C-...

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19th IEEE International Conference on Communication technology, ICCT 2019

作者： Jin, Xiaoyan Zhang, Jun Sun, Xinghua Zhang, Ping Cai, Shu Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Key Laboratory of Computer Application Technology Anhui Polytechnic University Wuhu241000 China

ISBN: (纸本)9781728105352

Mobile edge computing (MEC) technology has become a promising example for cloud radio access networks (CRAN) to provide close-range services, thereby reducing service delays and saving energy consumption. In this paper, we consider a multi-user MEC system and solve the problem of the computation offloading strategies and resource allocation policies. We set the total cost of delays and energy consumption as our optimization goal. However, getting an optimal strategy in a dynamic environment is challenging. Reinforcement learning (RL) aims at long-term cumulative rewards, which are essential for time-varying dynamic systems. Therefore, we propose an optimization framework based on deep RL to solve these problems. The deep neural network (DNN) is used to estimate the value function of the critics, thereby reducing the state space complexity of the optimization target. The actor part uses another DNN to represent a parametritis stochastic strategy and improve the strategy with the help of critics. Compared with other schemes, the simulation results show that the scheme significantly reduces the total cost. © 2019 IEEE.

关键词： Reinforcement learning

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Overview of the CCKS 2019 knowledge graph evaluation track: Entity, relation, event and QA

arXiv

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arXiv 2020年

作者： Han, Xianpei Wang, Zhichun Zhang, Jiangtao Wen, Qinghua Li, Wenqi Tang, Buzhou Wang, Qi Feng, Zhifan Zhang, Yang Lu, Yajuan Wang, Haitao Chen, Wenliang Shao, Hao Chen, Yubo Liu, Kang Zhao, Jun Wang, Taifeng Zhang, Kezun Wang, Meng Jiang, Yinlin Qi, Guilin Zou, Lei Hu, Sen Zhang, Minhao Lin, Yinnian Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence Beijing Normal University Beijing100875 China 305 Hospital of People’s Liberation Army Beijing100017 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Yidu Cloud Beijing100000 China Harbin Institute of Technology Shenzhen Guangdong518055 China Baidu Beijing100193 China School of Computer Science & Technology Soochow University Suzhou Jiangsu215006 China Gowild Suzhou Jiangsu215002 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Ant Financial Hangzhou Zhejiang310000 China School of Computer Science and Engineering Southeast University Nanjing Jiangsu211189 China Key Laboratory of Computer Network Information Integration Southeast University Nanjing Jiangsu211189 China Peking University Beijing100871 China

Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation track with 6 tasks and attracted more than 1,600 teams. In this paper we give an overview of the knowledge graph evaluation tract at CCKS 2019. By reviewing the task definition, successful methods, useful resources, good strategies and research challenges associated with each task in CCKS 2019, this paper can provide a helpful reference for developing knowledge graph applications and conducting future knowledge graph researches. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

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Evaluation of VRMS Homogeneity of the Magnetic Field in MRI with Numerical Methods

Evaluation of VRMS Homogeneity of the Magnetic Field in MRI ...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Xuewei Ping Mingyu Wang Xinghui Yin Xin Wang Yong Lv Qingbo Li College of Computer and Information HoHai University Nanjing China Jiangsu Key Construction Laboratory of Modern Measurement Technology and Intelligent System Huaiyin Normal University Huai'an China

ISBN: (数字)9780996007894

ISBN: (纸本)9781728146331

A numerical method based on tetrahedron mesh discretization to compute the VRMS homogeneity of the magnetic field in magnetic resonance imaging is proposed, which is more powerful when the region is not spherical. In the final of the paper, the VRMS homogeneity of a super-conducting magnet coil is computed with the numerical method.

关键词： Magnetic resonance imaging Magnetic fields Ellipsoids Magnetic field measurement Magnetic analysis Layout Nuclear magnetic resonance

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