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Satellite image catalog system improvement based on process innovation method

AIP Conference Proceedings 2018年第1期1977卷

作者： Andie Setiyoko Ruci Meiyanti Nurbojatmiko Syamsudin Dana Indra Sensuse Handrie Noprisson 1Remote Sensing Technology and Data Centre LAPAN Indonesia 2e-Government and e-Business Research Lab Faculty of Computer Science University of Indonesia Indonesia 3Department of Information System Faculty of Computer Science Mercu Buana University Indonesia

Satellite imagery catalog system is developed for the user’s need regarding with the utilization of satellite imagery for various applications. The aim of the system development is the dissemination of data, particularly at the national scope. The system has been established and continuously operated until recently. In order to maintain the customer loyalty and to raise customer satisfaction, the system improvement is required. Based on user requirement analysis, the system should be improved in terms of three factors, i.e. transaction time, product derivations, and additional data processing service. The existing business process will be analyzed by adopting the system improvement. The process is categorized as business process improvement. The new version of business processes will be operated in the catalog system by applying the developed information system methods. Various methods classified as organizational-approach were analyzed in this research, and the most suitable method for improving the system is process innovation method, considering the urgent user need for regional development planning and rapid development of satellite imagery technology. The objective of this research was the improvement of satellite image catalog system by using the most suitable process innovation method for the case study. The expected result is to increase the time efficiency of satellite image data service and its processing.

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Fake news propagate differently from real news even at early stages of spreading

arXiv

引用

arXiv 2018年

作者： Zhao, Zilong Zhao, Jichang Sano, Yukie Levy, Orr Takayasu, Hideki Takayasu, Misako Li, Daqing Wu, Junjie Havlin, Shlomo School of Reliability and Systems Engineering Beihang University Beijing100191 China Science and Technology on Reliability and Environmental Engineering Laboratory Beijing100191 China School of Economics and Management Beihang University Beijing100191 China Faculty of Engineering Information and Systems University of Tsukuba Tennodai Tsukuba Ibaraki305-8573 Japan Department of Physics Bar-Ilan University Ramat Gan5290002 Israel Sony Computer Science Laboratories 3-14-13 Higashi-Gotanda Tokyo141-0022 Japan Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8502 Japan Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Social media can be a double-edged sword for society, either as a convenient channel exchanging ideas or as an unexpected conduit circulating fake news through a large population. While existing studies of fake news focus on theoretical modeling of propagation or identification methods based on machine learning, it is important to understand the realistic mechanisms between theoretical models and black-box methods. Here we track large databases of fake news and real news in both, Weibo in China and Twitter in Japan from different culture, which include their complete traces of re-postings. We find in both online social networks that fake news spreads distinctively from real news even at early stages of propagation, e.g. five hours after the first re-postings. Our finding demonstrates collective structural signals that help to understand the different propagation evolution of fake news and real news. Different from earlier studies, identifying the topological properties of the information propagation at early stages may offer novel features for early detection of fake news in social media. Copyright © 2018, The Authors. All rights reserved.

关键词： Social networking (online)

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An improved computational framework using one stage filtration by incorporating knowledge in gene expression clustering 16

An improved computational framework using one stage filtrati...

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2016 International Conference on Artificial Intelligence and Robotics, ICAIR 2016 and 2016 International Conference on Automation, Control and Robotics Engineering, CACRE 2016

作者： Kasim, Shahreen Fudzee, Mohd Farhan Md. Salamat, Mohamad Aizi Ramli, Azizul Azhar Mahdin, Hairulnizam Abdullah, Mohd Hafizul Afifi Soft Computing and Data Mining Centre Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat86400 Malaysia Web Technology Department Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat86400 Malaysia

ISBN: (纸本)9781450342353

The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. Given a gene expression dataset, the challenge is to predict gene function given the intensity and redundancy of the data and the lack of similarity of expression profiles without degrading the analysis performance and also without assigning genes at random. At the same time, the computational method must be capable of producing highly compacted clusters with furthest separation, high consistency, and accuracy. In this paper, we present a new computational framework for clustering gene expression data. From this experiment, we can conclude that our new framework capable to improve the accuracy thus determined the dominant gene. © 2016 ACM.

关键词： Gene expression

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The FAIR Funder: A pilot programme to make it easy for funders to require and for grantees to produce FAIR data

arXiv

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arXiv 2019年

作者： Schultes, E.A. Sustkova, H. Pergl Wittenburg, P. Montesanti, A. Bloemers, S.M. De Waard, S.H. Musen, M.A. Graybeal, J.B. Hettne, K.M. Jacobsen, A. Pergl, R. Hooft, R.W.W. Staiger, C. Van Gelder, C.W.G. Knijnenburg, S.L. Van Arkel, A.C. Meerman, B. Wilkinson, M.D. Sansone, S.-A. Rocca-Serra, P. McQuilton, P. Gonzalez-Beltran, A.N. Aben, G.J.C. Henning, P. Alencar, S. Ribeiro, C. Silva, C.R.L. Sayão, L. Go Fair International Support and Coordination Office Leiden Netherlands Max Planck Computing & Data Facility Garching Germany Health Research Board Dublin Ireland ZonMw The Hague Netherlands Stanford University StanfordCA United States Leiden University Libraries Leiden Netherlands Leiden University Medical Center Leiden Netherlands Czech Technical University in Prague Faculty of Information Technology Czech Republic Dutch Techcentre for Life Sciences Utrecht Netherlands Castor Edc Amsterdam Netherlands Purple Polar Bear Utrecht Netherlands Go Fair Foundation Leiden Netherlands Centre for Plant Biotechnology and Genomics Upm - Inia Universidad Politécnica de Madrid Madrid Spain Department of Engineering Science University of Oxford Oxford United Kingdom Sydney Australia Universidade Federal Do Estado Do Rio de Janeiro Rio de Janeiro Brazil Fundação Oswaldo Cruz Rio de Janeiro Brazil

There is a growing acknowledgement in the scientific community of the importance of making experimental data machine findable, accessible, interoperable, and reusable (FAIR). Recognizing that high quality metadata are essential to make datasets FAIR, members of the GO FAIR Initiative and the Research data Alliance (RDA) have initiated a series of workshops to encourage the creation of Metadata for Machines (M4M), enabling any self-identified stakeholder to define and promote the reuse of standardized, comprehensive machine-actionable metadata. The funders of scientific research recognize that they have an important role to play in ensuring that experimental results are FAIR, and that high quality metadata and careful planning for FAIR data stewardship are central to these goals. We describe the outcome of a recent M4M workshop that has led to a pilot programme involving two national science funders, the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW). These funding organizations will explore new technologies to define at the time that a request for proposals is issued the minimal set of machine-actionable metadata that they would like investigators to use to annotate their datasets, to enable investigators to create such metadata to help make their data FAIR, and to develop data-stewardship plans that ensure that experimental data will be managed appropriately abiding by the FAIR principles. The FAIR Funders design envisions a data-management workflow having seven essential stages, where solution providers are openly invited to participate. The initial pilot programme will launch using existing computer-based tools of those who attended the M4M Workshop. © 2019, CC BY-SA.

关键词： Metadata

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Random analysis of statistical rough set

Random analysis of statistical rough set

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Tsang, Eric C.C. Zhao, Su-Yun Faculty of Information Technology Macau University of Science and Technology C-Macau China Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

ISBN: (纸本)9781509003891

Attribute reduction is an inevitable problem in machine learning and statistical learning. To improve the traditional rough set reduction, statistical rough sets is then proposed by introducing random sampling into the rough approximation. Random sampling is the main contribution of statistical rough sets. As a result, it is necessary to analyze the randomness of statistical rough sets. In this paper, we analyze and demonstrate the influence of the randomness in the process of attribute reduction by a large number of experiments to test the effectiveness and stability of the random sampling. © 2016 IEEE.

关键词： Sampling

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Publisher Correction: Extreme risk induced by communities in interdependent networks

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Communications Physics 2019年第1期2卷 1页

作者： Jiachen Sun Rui Zhang Ling Feng Christopher Monterola Xiao Ma Céline Rozenblat H. Eugene Stanley Boris Podobnik Yanqing Hu School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Institute for Molecular Engineering University of Chicago Chicago USA Institute of High Performance Computing A*STAR Singapore Singapore Department of Physics National University of Singapore Singapore Singapore Asian Institute of Management Metro Manila Philippines School of Data and Computer Science Sun Yat-sen University Guangzhou China Institute of Geography University of Lausanne Lausanne Switzerland Department of Physics Boston University Boston USA Faculty of Civil Engineering University of Rijeka Rijeka Croatia Faculty of Information Studies Novo Mesto Slovenia Zagreb School of Economics and Management Zagreb Croatia Luxembourg School of Business Luxembourg Luxembourg

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Educational video classification by using a transcript to image transform and supervised learning

Educational video classification by using a transcript to im...

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Workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Houssem Chatbri Marlon Oliveira Kevin McGuinness Suzanne Little Keisuke Kameyama Paul Kwan Alistair Sutherland Noel E. O’Connor Insight Centre for Data Analytics Dublin City University Ireland School of Computing Dublin City University Ireland Faculty of Engineering Information and Systems University of Tsukuba Japan School of Science & Technology University of New England NSW Australia

In this work, we present a method for automatic topic classification of educational videos using a speech transcript transform. Our method works as follows: First, speech recognition is used to generate video transcripts. Then, the transcripts are converted into images using a statistical cooccurrence transformation that we designed. Finally, a classifier is used to produce video category labels for a transcript image input. For our classifiers, we report results using a convolutional neural network (CNN) and a principal component analysis (PCA) model. In order to evaluate our method, we used the Khan Academy on a Stick dataset that contains 2,545 videos, where each video is labeled with one or two of 13 categories. Experiments show that our method is effective and strongly competitive against other supervised learning-based methods.

关键词： Principal component analysis Transforms Visualization Feature extraction Training Convolutional neural networks Histograms

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Support Vector Regression Algorithm Modeling to Predict the Availability of Foodstuff in Indonesia to Face the Demographic Bonus

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Journal of Physics: Conference Series 2018年第1期1028卷

作者： Sari Devia Agustina Mustakim Okfalisa Celsa Bella Muhammad Anang Ramadhan Departemen of Information System UIN Sultan Syarif Kasim Riau Pekanbaru 28293 Indonesia Research Group of Puzzle Research Data Technology Faculty of Science and Technology UIN Sultan Syarif Kasim Riau Pekanbaru 28293 Indonesia Laboratory of Data Mining Departemen of Information System UIN Sultan Syarif Kasim Riau Pekanbaru 28293 Indonesia Departemen of Informatic Engineering UIN Sultan Syarif Kasim Riau Pekanbaru 28293 Indonesia

The demographic bonus becomes a valuable phenomenon for Indonesian. One of the positive effects of this phenomenon is the increase of productive age proclaimed which will be the future of Indonesian economy. The agricultural sector plays an important role of the overall national economy which is indicated by an increase from year to year. However, the level of nutritional adequacy declined by a few percent each year due to an increase in the number of people who are not balanced by increased demand for food. In this case the government is expected to determine the policy priorities related to Demographic Bonus issues by predicting the future. Computing and data mining technologies play an important role in prediction cases by drawing conclusions based on regression lines. The technique is called Support Vector Regression, which is able to handle some cases of statistical data. Three determinant attributes used in this research are (1) Harvest Area; (2) Number of Harvest Production; and (3) Food Productivity, become the main reference for 714 data from 1998-2015 in 34 Provinces in Indonesia containing 7 types of crops. Three distribution data experiments conducted using K-Fold Cross Validation have the highest accuracy on Fold-1 with correlation coefficient value (R) of 92% with the smallest error value at fold-1 with MSE value of 14%. Predicted results show a decline in the number of food production in almost every province in Indonesia. From the experimental results, it is known that the biggest contributor of food products is Java Island, especially in East Java. Almost every kind of palawija plant, East Java plays an important role in the production of food needed in Indonesia.

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HDFS enabled storage and management of remote sensing data 2

HDFS enabled storage and management of remote sensing data

引用

2nd IEEE International Conference on Computer and Communications, ICCC 2016

作者： Kou, Weili Xuejing, Yang Changxian, Liang Xie, Changbo Gan, Shu School of Computer and Information Southwest Forestry University Kunming China Yunnan Big Data Geo-information Technology Co. Ltd. Kunming China Faculty of Land Resources and Engineering Kunming University of Science and Technology Yunnan China

ISBN: (纸本)9781467390262

The continuously growing volume of massive remote sensing data raised huge challenges on storage space and querying efficiency. In this paper, a new management model of remote sensing data has been proposed to address these issues of system architecture, data storage strategies, and functionalities based on Hadoop Distributed File System (HDFS). The model is capable of relieving the overloading problems of the single NameNode server in HDFS by taking a dual storage mechanism and a simulating operations method. On one hand, Relational database Management System (RDBMS) and HDFS are separately taken to store and manage image files and related metadata of remote sensing data;on the other hand, operations of file systems are simulated by RDBMS. The study results show the model could improve management and storage efficiency of remote sensing data. © 2016 IEEE.

关键词： Storage efficiency

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A note on the possibility of incomplete theory

arXiv

引用

arXiv 2017年

作者： Geurdes, Han Nagata, Koji Nakamura, Tadao Farouk, Ahmed Geurdes Data Science KvK64522202 CvdLinstraat 164 Den Haag2593NN Netherlands Department of Physics Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Information and Computer Science Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Japan Computer Sciences Department Faculty of Computers and Information Mansoura University Scientific Research Group Egypt

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