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E-Business, Airport Development and Its Impact on the Increasing of Information of Communication Development in Indonesia

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Journal of Physics: Conference Series 2018年第1期1007卷

作者： MI Setiawan C Hasyim N Kurniasih D Abdullah D Napitupulu R Rahim A Sukoco I Dhaniarti J Suyono IN Sudapet RD Nasihien DAR Wulandari Reswanda SW Mudjanarko Sugeng MBN Wajdi Narotama University Department of Civil Engineering Surabaya Indonesia Universitas Darul Ulum Jombang Indonesia Universitas Padjadjaran Faculty of Communication Science Bandung Indonesia Universitas Malikussaleh Department of Informatics Aceh Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Malaysia Narotama University Department of Management Surabaya Indonesia Narotama University Department of Law Surabaya Indonesia Universitas Islam '45' Bekasi Indonesia STAI Miftahul 'Ula Nglawak Kertosono Indonesia

The increasing number of the internet usage by households have an effect on the tourism sector. On the other hand, the aviation industry is growing as one of the development centers. This study aims to analyze the impact of information and communication development to airport performance in Indonesia. This is a correlation research with 151 of airports in Indonesia as a population. The sampling technique was done by using total sampling. The results of correlation (R) indicates the Gross Regional Domestic Product (GRDP) of Information and Communication has a relatively strong relationship with the Airport Performance. Meanwhile the results of Adjusted R test shows that they are other factors in increasing GRDP of Information and Communication besides Airport Performance. It shows the low impact of Information and Communication GRDP to the Airport Performance.

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E-Business, The impact of the Regional Government Development (APBD) on Information and Communication Development in Indonesia

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Journal of Physics: Conference Series 2018年第1期1007卷

作者： M I Setiawan C Hasyim N Kurniasih D Abdullah D Napitupulu R Rahim A Sukoco I Dhaniarti J Suyono IN Sudapet RD Nasihien DAR Wulandari Reswanda SW Mudjanarko Sugeng MBN Wajdi Narotama University Department of Civil Engineering Surabaya Indonesia Universitas Darul Ulum Jombang Indonesia Universitas Padjadjaran Faculty of Communication Science Bandung Indonesia Universitas Malikussaleh Department of Informatics Aceh Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Malaysia Narotama University Department of Management Surabaya Indonesia Narotama University Department of Law Surabaya Indonesia Universitas Islam '45' Bekasi Indonesia STAI Miftahul 'Ula Nglawak Kertosono Indonesia

The decline of development delived from natural resources in several regions in Indonesia requires local governments to make a breakthrough in increasing revenues beyond natural resources, including through increased revenue in the field of information technology. The purpose of this study is to analyze the impact of the regional government development (APBD) on information and communication development in Indonesia. By using correlational research, it is known that all of APBD sub variables have a high correlation in increasing Gross Regional Domestic Product (GRDP) of Information and Communication in Indonesia, only sub-variable of surplus/deficit development, unexpected development sub-variable, subvariable of development aid to province/regency/city and village government, and sub-variable of development special allocations that have not a significant correlation to increase of GRDP of Information and Communication in Indonesia. There is 86,7% of APBD variable influence the increase of GRDP of Information and Communication, while the increase of 15,3% of Information and Communication GRDP influenced by other factors aside from APBD. It shows the strong impact of APBD on increasing GRDP of Information and Communication

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Thermal-aware relocation of servers in green data centers

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Frontiers of Information technology & Electronic Engineering 2015年第2期16卷 119-134页

作者： Muhammad Tayyab CHAUDHRY T. C. LING S. A. HUSSAIN Xin-zhu LU Department of Computer System & Technology Faculty of Computer Science & Information Technology University of Malaya Kuala Lumpur 50603 Malaysia Department of Computer Science COMSATS Institute of lnformation Technology Lahore 54000 Pakistan

Rise in inlet air temperature increases the corresponding outlet air temperature from the server. As an added effect of rise in inlet air temperature, some active servers may start exhaling intensely hot air to form a hotspot. Increase in hot air tem- perature and occasional hotspots are an added burden on the cooling mechanism and result in energy wastage in data centers. The increase in inlet air temperature may also result in failure of server hardware. Identifying and comparing the thermal sensi- tivity to inlet air temperature for various servers helps in the thermal-aware arrangement and location switching of servers to minimize the cooling energy wastage. The peak outlet temperature among the relocated servers can be lowered and even be homogenized to reduce the cooling load and chances of hotspots. Based upon mutual comparison of inlet temperature sensitivity of heterogeneous servers, this paper presents a proactive approach for thermal-aware relocation of data center servers. The experimental results show that each relocation operation has a cooling energy saving of as much as 2.1 kW·h and lowers the chances of hotspots by over 77%. Thus, the thermal-aware relocation of servers helps in the establishment of green data centers.

关键词： Servers Green data center Thermal-aware Relocation

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Corruption Cases Mapping Based on Indonesia's Corruption Perception Index

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Journal of Physics: Conference Series 2017年第1期801卷

作者： Noerlina L A Wulandhari Sasmoko A M Muqsith M Alamsyah School of Information System Bina Nusantara University Indonesia School of Computer Science Bina Nusantara University Indonesia Faculty of Humanities Bina Nusantara University Indonesia Research Interest Group in Education Technology Bina Nusantara University Indonesia

Government plays an important role in nation economic growth. Nevertheless, there are still many occurrences of government officers abusing their offices to do an act of corruption. In this order, the central government should pay attention to every area in the nation to avoid corruption case. Meanwhile, the news media always constantly preach about corruption case, this makes the news media relevant for being one of the sources of measurement of corruption perception index (CPI). It is required to map the corruption case in Indonesia so the central government can pay attention to every region in Indonesia. To develop the mapping system, researchers use Naïve Bayes Classifier to classify which news articles talk about corruption and which news articles are not, before implementing a Naïve Bayes Classifier there are some text processing such as tokenizing, stopping, and stemming.

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Fast Learning for Big Data Using Dynamic Function

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IOP Conference Series: Materials Science and Engineering 2017年第1期190卷

作者： T Alwajeeh A F Alharthi R F Rahmat R Budiarto Dept. of Computer Science & Engineering College of CS&IT Albaha University Albaha P.O. Box 1988 Saudi Arabia Dept. of Computer Information System College of CS&IT Albaha University Albaha P.O. Box 1988 Saudi Arabia Department of Information Technology Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia

This paper discusses an approach for fast learning in big data. The proposed approach combines momentum factor and training rate, where the momentum is a dynamic function of the training rate in order to avoid overshoot weight to speed up training time of the back propagation neural network engine. The two factors are adjusted dinamically to assure the fast convergence of the training process. Experiments on 2-bit XOR parity problem were conducted using Matlab and a sigmoid function. Experiments results show that the proposed approach signifcantly performs better compare to the standard back propagation neural network in terms of training time. Both, the maximum training time and the minimum training time are significantly faster than the standard algorithm at error threshold of 10-5.

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Using conservative estimation for conditional probability instead of ignoring infrequent case

Using conservative estimation for conditional probability in...

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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Masato Kikuchi Eiko Yamamoto Mitsuo Yoshida Masayuki Okabe Kyoji Umemura Department of Computer Science and Engineering Toyohashi University of Technology Toyohashi Japan Department of Economics and Information Gifu Shotoku Gakuen University Gifu Japan Faculty of Management and Information System Prefectural University of Hiroshima Hiroshima Japan

ISBN: (纸本)9781509016372

There are several estimators of conditional probability from observed frequencies of features. In this paper, we propose using the lower limit of confidence interval on posterior distribution determined by the observed frequencies to ascertain conditional probability. In our experiments, this method outperformed other popular estimators.

关键词： Maximum likelihood estimation Urban areas Frequency estimation Smoothing methods Mutual information Probability

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The information system of pregnant womans’ health nutritious based on android (Case study: Puskesmas Mopah Merauke)

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AIP Conference Proceedings 2017年第1期1813卷

作者： Sri Murniani Angelina Letsoin Yuliana Kolyaan Dedy Cahyadi Information System Department Faculty of Engineering Musamus University Jl. Kamizaun Mopah Merauke Papua Indonesia Informatics Department Faculty of Engineering Musamus University Jl. Kamizaun Mopah Merauke Papua Indonesia Computer Science Department Computer Science and Information Technology Faculty Mulawarman University Jl. Barong tongkok no. 2 Gedung A FMIPA lt3. Samarinda Indonesia

The cause of maternal mortality can be divided into two, the direct cause and indirect cause. One of the indirect causes is too difficult to reach health services and the lack of pregnancy knowledge. On the other hand, Android smartphone development of communications technology has increased compared to users of other devices, e.g. blackberry, which has dropped from 11.5% to 4.8% while the android market share has grown from 46.9% up to 68.1%. This increasing is being an opportunity for the software developers to design some software based on Android. The aim of this study was to facilitate the pregnant women to find out some information about the nutritional health, abstinence, calculate gestational age and nutrition based on the period of pregnancy. The information system was designed by using UML, the Eclipse IDE with the java programming language, MySQL as the database. The testing results showed that the nutrition information system based on android could help pregnant women to obtain health nutrition information such as nutrition, calories, dietary restrictions that should be avoided during the first month to the nine month of pregnancy, and the calculation of gestation.

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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A second update on mapping the human genetic architecture of COVID-19

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Nature 2023年第7977期621卷 E7-E26页

作者： Masahiro Kanai Christine Stevens Benjamin M. Neale Mark Daly Andrea Ganna Amy Trankiem Mary K. Balaconis Samuli Ripatti Lindo Nkambul Mark J. Daly Sam Bryant Matthew Solomonson Shea J. Andrews Mattia Cordioli Juha Karjalainen Juha Mehtonen Matti Pirinen Andrea Corbetta Bradley Jermy Kati Donner Katja Kivinen Aarno Palotie Mari Kaunisto Kumar Veerapen Konrad J. Karczewski Alicia R. Martin Elizabeth G. Atkinson Kristin Tsuo Nikolas Baya Rahul Gupta Raymond K. Walters Duncan S. Palmer Gopal Sarma Nathan Cheng Wenhan Lu Claire Churchhouse Jacqueline I. Goldstein Daniel King Wei Zhou Cotton Seed Hilary Finucane F. Kyle Satterstrom Gita A. Pathak Akiko Iwasaki Karolina Chwialkowska Karolina Chwiałkowska Brooke N. Wolford Hajar Fauzan Ahmad Shea Andrews Kathrin Aprile von Hohenstaufen Puoti Palwende R. Boua Guillaume Butler-Laporte Celia M. T. Greenwood Bartha M. Knoppers Mark Lathrop Carmen L. Cadilla Francesca Colombo Nicole Dueker Chloe Kirk Atanu Kumar Dutta Yasser M. El-Sherbiny Jehan J. El-Jawhari Doaa Shahin Ahmed Elzeiny Tamer A. Mansour Madonna M. Eltoukhy Attia A. S. Mohamed Sahar Esmaeeli Annika Faucon Israel Fernández Cadenas Israel Fernandez-Cadenas Laia Llucià-Carol Natalia Cullell Elena Muiño Jara Cárcel-Márquez Margherita Francescatto Laurent Francioli Lindokuhle Nkambule Jatin Arora Soumya Raychaudhuri Caroline Cusick Lude Franke Macarena Fuentes Macarena Fuentes-Guajardo David Gómez Cabrero Emi N. Harry Philip Jansen József L. Szentpéteri Réka Várnai Elżbieta Kaja Athanasios Kousathanas Dorote Pasko Mark J. Caulfield Alex Stuckey Christopher A. Odhams Susan Walker Daniel Rhodes Richard H. Scott Loukas Moutsianas Afshan Siddiq Peter Goddard Sally Donovan Tala Zainy Fiona Maleady-Crowe Linda Todd Shahla Salehi Greg Elgar Georgia Chan Prabhu Arumugam Christine Patch Augusto Rendon Tom A. Fowler Jose E. Krieger Sanjay K. Patel Audrey Lemaçon Sophie Limou Pietro Lió Eirini Marouli Charles Hinds Minttu M. Marttila Carolina Medina-Gómez Isabelle Migeotte Soumyajit Mondal Andrés Moreno-Estrada Leire Moya Tomok Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland Public Health Faculty of Medicine University of Helsinki Helsinki Finland Analytic & Translational Genetics Unit Massachusetts General Hospital Boston MA USA Stanley Center for Psychiatric Research & Program in Medical and Population Genetics Cambridge UK Institute for Molecular Medicine Finland (FIMM) Univerisity of Helsinki Helsinki Finland University of California San Francisco San Francisco CA USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Department of Psychiatry School of Medicine Yale University New Haven CT USA Veteran Affairs Connecticut Healthcare System West Haven CT USA Yale University New Haven CT USA Centre for Bioinformatics and Data Analysis Medical University of Bialystok Bialystok Poland Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA Faculty of Industrial Sciences and Technology Universiti Malaysia Pahang Pahang Malaysia Icahn School of Medicine at Mount Sinai New York NY USA Genolier Innovation Network and Hub Swiss Medical Network Genolier Healthcare Campus Genolier Switzerland Immediate Independent Multidisciplinary Med & Biotech Research Association Milan Italy Clinical Research Unit of Nanor

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Motion Prejudgment Dependent Mixture system Noise in system Model for Tennis Ball 3D Position Tracking by Particle Filter

Motion Prejudgment Dependent Mixture System Noise in System ...

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Yuan Wang Xina Cheng Norikazu Ikoma Masaaki Honda Takeshi Ikenaga Graduate School of Information Production and System Waseda University Kita-Kyushu Fukuoka Japan Dept. of Computer and Information Engineering Nippon Institute of Technology Saitama Japan Faculty of Sport Sciences Waseda University Tokyo Japan

ISBN: (纸本)9781509026791

In tennis game analysis, the 3D position of ball plays a crucial role in score judgment and player evaluation. When tracking the tennis ball in 3D space, high speed and abrupt motion change of the tennis ball are the main problems which make it difficult to predict the near future course of the ball. Aiming at solving above two problems, we propose a system model based on an elaborated mixture system noise. The mixture system noise consists of general change noise and adaptive abrupt change noise which is dependent on motion prejudgment result of tennis ball. The motion prejudgment method is carried out by the current state of ball and players. The motion of ball is classified into general motion and three abrupt motions, including smash, bounce and hit the net. Experiments based on 13 HDTV video sequences, which were recorded by four cameras located at four corners of the tennis court outside in a cloudy day including two players were used to explore the performance of the proposed method. The tracking success rate is 81.14%, gaining 27.64% improvement compared with conventional work.

关键词： Sports equipment Three-dimensional displays Target tracking Particle filters Cameras Games

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