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Graph stochastic neural networks for semi-supervised learning 20

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Graph stochastic neural networks for semi-supervised learnin...

Proceedings of the 34th International Conference on Neural information Processing systems

作者： Haibo Wang Chuan Zhou Xin Chen Jia Wu Shirui Pan Jilong Wang Department of Computer Science and Technology Tsinghua University Beijing China and Institute for Network Sciences and Cyberspace Tsinghua University Beijing China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute for Network Sciences and Cyberspace Tsinghua University Beijing China Faculty of Science and Engineering Macquarie University Sydney Australia Faculty of Information Technology Monash University Melbourne Australia

ISBN: (纸本)9781713829546

Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However, most existing models learn a deterministic classification function, which lack sufficient flexibility to explore better choices in the presence of kinds of imperfect observed data such as the scarce labeled nodes and noisy graph structure. To improve the rigidness and inflexibility of deterministic classification functions, this paper proposes a novel framework named Graph Stochastic Neural Networks (GSNN), which aims to model the uncertainty of the classification function by simultaneously learning a family of functions, i.e., a stochastic function. Specifically, we introduce a learnable graph neural network coupled with a high-dimensional latent variable to model the distribution of the classification function, and further adopt the amortised variational inference to approximate the intractable joint posterior for missing labels and the latent variable. By maximizing the lower-bound of the likelihood for observed node labels, the instantiated models can be trained in an end-to-end manner effectively. Extensive experiments on three real-world datasets show that GSNN achieves substantial performance gain in different scenarios compared with state-of-the-art baselines.

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Detecting spam campaign in twitter with semantic similarity

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Journal of Physics: Conference Series 2020年第1期1447卷

作者： M Mostafa A Abdelwahab H M Sayed Department of Information System Faculty of Computer and Information Menoufia University Menoufia Egypt Department of Information Systems College of Computer science and Information Technology King Faisal University Saudi Arabia

Twitter is a widespread supply for real-time news distribution between individuals. Furthermore, spammers could post any kinds of spam content to users, and a variant of incidents are committed on Twitter against users. These threats aren't restricted to the social media platforms however they threaten the safety of Twitter users. Most of the researches use deep learning techniques to detect Twitter spammer activities. The traditional solutions check the behavior of each account or campaign of similar purpose accounts. The number of solutions concentrate on detecting spam campaign based on URL only and ignoring text in a tweet. In this paper, the lightweight framework is proposed to take tweet text into consideration for optimizing spam campaign detection methods based on deep learning techniques. The main contribution of this work summarized in two key points. First one is to summarize text of the tweets to cluster them. The second one is to find similar tweets based on Siamese Recurrent Network. Experimental results show the ability of the proposed technique to extract the right candidate campaign and classify them as spam or not with high recall and precision.

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Most productive scale size of China’s regional R&D value chain: A mixed structure network

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arXiv 2019年

作者： Assani, Saeed Jiang, Jianlin Assani, Ahmad Yang, Feng College of Science Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Management University of Science and Technology of China Hefei230026 China Faculty of Computer Science and Business Computer Systems Karlsruhe University of Applied Science Karlsruhe76133 Germany

This paper offers new mathematical models to measure the most productive scale size (MPSS) of production systems with mixed structure networks (mixed of series and parallel). In the first property, we deal with a general multi-stage network which can be transformed, using dummy processes, into a series of parallel networks. In the second property, we consider a direct network combined with series and parallel structure. In this paper, we propose new models to measure the overall MPSS of the production systems and their internal processes. MPSS decomposition is discussed and examined. As a real-life application, this study measures the efficiency and MPSS of research and development (R&D) activities of Chinese provinces within an R&D value chain network. In the R&D value chain, profitability and marketability stages are connected in series, where the profitability stage is composed of operation and R&D efforts connected in parallel. The MPSS network model provides not only the MPSS measurement but also values that indicate the appropriate degree of intermediate measures for the two stages. Improvement’s strategy is given for each region based on the gap between the current and the appropriate level of intermediate measures. Our findings show that the marketability efficiency values of Chinese R&D regions were low, and no regions are operated under the MPSS. As a result, most Chinese regions performed inefficiently regarding both profitability and marketability. This finding provides initial evidence that the generally lower profitability and marketability efficiency of Chinese regions is a severe problem that may be due to wasted resources on production and R&D. Copyright © 2019, The Authors. All rights reserved.

关键词： Efficiency

Hospital Readmission Prediction using Discriminative patterns

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Hospital Readmission Prediction using Discriminative pattern...

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Sea Jung Im Yue Xu Jason Watson Ann Bonner Helen Healy Wendy Hoy School of Computer Science Queensland University of Technology Brisbane Australia School of Information Systems Queensland University of Technology Brisbane Australia School of Nursing and Midwifery Griffith University Brisbane Australia Metro North Hospital & Health Service Queensland Health Brisbane Australia Faculty of Medicine The University of Queensland Brisbane Australia

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

Avoidable hospital readmission is problematic as it increases the burden on healthcare systems, leads to a shortage of hospital beds and impacts on the costs of healthcare. Various machine learning algorithms have been applied to predict patient readmissions. However, existing literature has only focused on individual features of health conditions without consideration of associations between features. This paper proposes discriminative pattern-based features as a technique to improve readmission prediction. First, discriminative patterns that occur disproportionately between two classes: readmission and non-readmission, were generated based on hospital electronic health records. Second, the patterns were fed as features into a classification model for readmission prediction. Then, we have evaluated these discriminative pattern-based features in three datasets: diabetes, chronic kidney disease and all diseases. Experiments with each dataset showed that the features of chronic disease cohorts have fewer differences between the readmission and the non-readmission classes than the all-diseases cohort. Our proposed pattern-based model improved the prediction performance in terms of AUC (Area Under the receiver operating characteristic curve) by about 12% compared with the baseline models for the all-disease cohort, however, it showed little improvement for either diabetes or chronic kidney disease datasets.

关键词： Hospitals Prediction algorithms Australia Predictive models Diseases Medical diagnostic imaging Diabetes

Estimating and decomposing most productive scale size in parallel DEA networks with shared inputs: A case of China’s Five-Year Plans

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arXiv 2019年

Attaining the optimal scale size of production systems is an issue frequently found in the priority questions on management agendas of various types of organizations. Determining the most productive scale size (MPSS) allows the decision makers not only to know the best scale size that their systems can achieve but also to tell the decision makers how to move the inefficient systems onto the MPSS region. This paper investigates the MPSS concept for production systems consisting of multiple subsystems connected in parallel. First, we propose a relational model where the MPSS of the whole system and the internal subsystems are measured in a single DEA implementation. Then, it is proved that the MPSS of the system can be decomposed as the weighted sum of the MPSS of the individual subsystems. The main result is that the system is overall MPSS if and only if it is MPSS in each subsystem. MPSS decomposition allows the decision makers to target the non-MPSS subsystems so that the necessary improvements can be readily suggested. An application of China’s Five-Year Plans (FYPs) with shared inputs is used to show the applicability of the proposed model for estimating and decomposing MPSS in parallel network DEA. Industry and Agriculture sectors are selected as two parallel subsystems in the FYPs. Interesting findings have been noticed. Using the same amount of resources, the Industry sector had a better economic scale than the Agriculture sector. Furthermore, the last two FYPs, 11th and 12th, were the perfect two FYPs among the others. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision making

Antenna Array Enabled Space/Air/Ground Communications and Networking for 6G

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arXiv 2021年

作者： Xiao, Zhenyu Han, Zhu Nallanathan, Arumugam Dobre, Octavia A. Clerckx, Bruno Choi, Jinho He, Chong Tong, Wen The School of Electronic and Information Engineering Beihang University Beijing100191 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom The Faculty of Engineering and Applied Science Memorial University St. John’sNLA1C 5S7 Canada The Communications and Signal Processing Group Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The School of Information Technology Deakin University GeelongVIC3220 Australia The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The Wireless Advanced System Competency Centre Huawei Technologies Co. Ltd. OttawaONK2K 3J1 Canada

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays (e.g., phased arrays, digital arrays, and reconfigurable intelligent surfaces, etc.) with various beamforming technologies (e.g., analog beamforming, digital beamforming, hybrid beamforming, and passive beamforming, etc.) in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/ai

关键词： Beamforming

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Preface

Studies in Big Data

Studies in Big Data 2021年 73卷 v-vii页

作者： Chakraborty, Chinmay Banerjee, Amit Kolekar, Maheshkumar H. Garg, Lalit Chakraborty, Basabi Department of Electronics and Communication Engineering Birla Institute of Technology Ranchi Mesra India Department of Electrical Engineering Indian Institute of Technology Patna Patna India Faculty of Software and Information Science Iwate Prefectural University Takizawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta Msida Malta

Implementation of the algorithm for constructing homogeneous approximations of nonlinear control systems

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arXiv 2021年

作者： Sklyar, Grigory Barkhayev, Pavel Ignatovich, Svetlana Rusakov, Viktor Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin Zolnierska 49 Szczecin71-210 Poland Institute of Mathematics University of Szczecin Wielkopolska 15 Szczecin70-451 Poland Department of Mathematical Sciences Norwegian University of Science and Technology Høgskoleringen 1 Trondheim7491 Norway B.Verkin Institute for Low Temperature Physics and Engineering Academy of Sciences of Ukraine Nauki Ave. 47 Kharkiv61103 Ukraine Department of Applied Mathematics V. N. Karazin Kharkiv National University Svobody Sq. 4 Kharkiv61022 Ukraine

We present a "calculator" for constructing a homogeneous approximation of nonlinear control systems, which is based on the algebraic approach developed by the authors in their previous papers. This approach mainly uses linear algebraic and combinatorial tools, so, it is perfectly adapted to computer realization. We describe the algorithm and discuss its capabilities and limitations. We present its implementation as a web application and show by example how this app works. © 2021, CC BY.

关键词： Algebra

Analysis of delay factors on drilling projects on PT China Oilfields Services Limited

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IOP Conference Series: Earth and Environmental science 2021年第1期729卷

作者： W Sardjono W G Perdana R Ansori D N Utama Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management BINA NUSANTARA University Jakarta Indonesia 11480 Post Graduate Program School of Environmental Science University of Indonesia Jakarta Indonesia Graduate Program Master of Business Administration Faculty of Economic and Business University of Gadjahmada Yogyakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In carrying out drilling projects at PT China Oilfields Services Limited ( COSL) Indo, especially Project 1 to Project 4, there were a mismatch between the initial plan of the project and the actualisation in the field because there were inhibiting factors in implementing the project. The purpose of the research was to look the factors that caused for the additional time in well drilling project at PT. COSL Indo, to build a relationship model of these factors, and formulate a strategy for the company to be able to overcome the occurrence of additional time in the future project. Data analysis was conducted by using a factor analysis method and the location of the research was carried out in just one company with 102 valid respondents. The results of the research indicate that there are four factors that influence the time gap, namely Project Management Activities, Risk Analysis and Procurement, Manage Stairs, and Project Planning Development. By knowing the causes of the delay, companies can find the best solutions for future learning and the impact on environmental, social and economic problems can also be anticipated so that sustainable development occurs in the context of petroleum management.

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