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Biclustering Method to Capture the Spatial Pattern and to Identify the Causes of Social Vulnerability in Indonesia: A New Recommendation for Disaster Mitigation Policy

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Procedia Computer Science 2019年 157卷 31-37页

作者： Puspita Anggraini Kaban Robert Kurniawan Rezzy Eko Caraka Bens Pardamean Budi Yuniarto Sukim Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia College of Informatics Chaoyang University of Technology Taichung City 41349 Taiwan (R.O.C.) Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Geographically, Indonesia is a meeting point of three continental plates. Scilicet, the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate. Therefore, Indonesia is part of the infamous volcanic zone called the ”Ring of Fire” and one of the areas prone to natural disasters such as volcanic eruptions, earthquakes, tsunamis, floods, and landslides. This study aims to capture the spatial pattern and identify the causes of social vulnerability in the districts/cities in Indonesia using the biclustering method. The data is extracted from the Indonesian National Socio-Economic Survey (SUSENAS) by BPS-Statistics in 2014. The biclustering result indicates that each district/city has its own social vulnerability characteristics and shows that the vulnerable aspects of each district/city are different. The adjacent observations tend to have social vulenrability characteristics. The results of this study can be used as a reference for national disaster mitigation policy in Indonesia.

关键词： biclustering disaster mitigation social vulnerability SoVI spatial pattern

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Prototype Selection Using Self-Organizing-Maps and Entropy for Overlapped Classes and Imbalanced Data

Prototype Selection Using Self-Organizing-Maps and Entropy f...

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International Joint Conference on Neural Networks

作者： Marcio Rubbo Leandro A. Silva Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Sao Paulo SP Brazil Computing and Informatics Faculty & Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Sao Paulo SP Brazil

The k nearest neighbor (kNN) is a traditional supervised classifier used in data mining tasks. However, when used in real applications, mainly in a dataset with class imbalance or class overlap, kNN suffers with problems in accuracy performance. In this paper, we propose three prototype selection methods using self-organizing maps (SOM) and information entropy to increase the effectiveness of the kNN classifier in datasets with these conditions. The methods, named SOMEntropyKnn, were able to increase the effectiveness of the kNN classifier in all the 14 datasets used in the experiment, increasing the accuracy performance from datasets with imbalance or overlap problems.

关键词： Prototypes Entropy Self-organizing feature maps Neurons Training Information entropy Electrical engineering

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The health of things for classification of protein structure using improved grey wolf optimization

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The Journal of Supercomputing 2018年第2期76卷 1226-1241页

作者： Prerna Sharma Apoorva Gupta Aastha Aggarwal Deepak Gupta Ashish Khanna Aboul Ella Hassanien Victor Hugo C. de Albuquerque Maharaja Agrasen Institute of Technology GGSIPU Dwarka India Faculty of Computers and Information Cairo University Cairo Egypt Graduate Program in Applied Informatics University of Fortaleza Fortaleza Brazil

In the field of computational biology, prediction of high-resolution protein structure is regarded as a major challenge. Physical and chemical properties of the protein structure determine its quality and differentiate native structures from predicted structures. Various machine learning classification models are studied with six physical and chemical properties to classify the root mean square deviation of the protein structure. This work proposes an improved version of a meta-heuristic technique named grey wolf optimization (IGWO), which is an extension of traditional grey wolf optimization (GWO) for the feature selection. The proposed novel IGWO ascertains optimal subset of features, and further, four machine learning classifiers have been used for efficient prediction of protein structure. Artificial neural network classifier predicts the protein structure with a maximum approximate accuracy of 91%. The experimental result reveals that the proposed meta-heuristic technique is stable enough to maximize the accuracy and minimize the number of optimal features. In this paper, the result of the proposed technique has been compared with other related evolutionary techniques and the proposed optimizer outperforms all other techniques. The dataset used in the study is available at http://***/RMSDClassification-DS.

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Valley notch filter in a graphene strain superlattice: a Green’s function and machine learning approach

arXiv

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arXiv 2019年

作者： Torres, V. Silva, P. De, E.A.T. Souza Silva, L.A. Bahamon, D.A. Instituto de Física Universidade Federal Fluminense Niterói Av. Litorânea sn RJ24210-340 Brazil MackGraphe Mackenzie Presbyterian University Rua da Consolação 930 SP01302-907 Brazil School of Computing and Informatics Graduate Program in Electrical Engineering and Computing

The valley transport properties of a superlattice of out-of-plane Gaussians deformations are calculated using a Green’s function and a Machine Learning approach. Our results show that periodicity significantly improves the valley filter capabilities of a single Gaussian deformation, these manifest themselves in the conductance as a sequence by valley filter plateaus. We establish that the physical effect behind the observed valley notch filter is the coupling between counter-propagating transverse modes;the complex relationship between the design parameters of the superlattice and the valley filter effect make difficult to estimate in advance the valley filter potentialities of a given superlattice. With this in mind, we show that a Deep Neural Network can be trained to predict valley transmission with a precision similar to the Green’s function but with much less computational effort. Copyright © 2019, The Authors. All rights reserved.

关键词： Landforms

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Exploring Data Augmentation to Improve Music Genre Classification with ConvNets

Exploring Data Augmentation to Improve Music Genre Classific...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rafael L. Aguiar Yandre M.G. Costa Carlos N. Silla Postgraduate Program in Informatics (PPGIa) Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil Graduate Program in Computer Science (PCC) State University of Maringá (UEM) Maringá Brazil

In this work we address the automatic music genre classification as a pattern recognition task. The content of the music pieces were handled in the visual domain, using spectrograms created from the audio signal. This kind of image has been successfully used in this task since 2011 by extracting handcrafted features based on texture, since it is the main visual attribute found in spectrograms. In this work, the patterns were described by representation learning obtained with the use of convolutional neural network (CNN). CNN is a deep learning architecture and it has been widely used in the pattern recognition literature. Overfitting is a recurrent problem when a classification task is addressed by using CNN, it may occur due to the lack of training samples and/or due to the high dimensionality of the space. To increase the generalization capability we propose to explore data augmentation techniques. In this work, we have carefully selected strategies of data augmentation that are suitable for this kind of application, which are: adding noise, pitch shifting, loudness variation and time stretching. Experiments were conducted on the Latin Music Database (LMD), and the best obtained accuracy overcame the state of the art considering approaches based only in CNN.

关键词： Spectrogram Music Machine learning Training Task analysis Visualization Multiple signal classification

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Association of Fine Motor Loss and Allodynia in Fibromyalgia: An fNIRS Study (vol 50, pg 664, 2017)

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JOURNAL OF MOTOR BEHAVIOR 2018年第6期50卷 X-X页

作者： Eken, A. Gokcay, D. Yilmas, C. Baskak, B. Baltaci, A. Kara, M. Biomedical Engineering Department Düzce University Düzce Turkey Medical Informatics Department Informatics Institute Middle East Technical University Ankara Turkey Neuroscience Graduate Program Bilkent University Ankara Turkey Department of Psychiatry Ankara University Faculty of Medicine Ankara Turkey Ankara University Brain Research Center Ankara Turkey Department of Physical and Rehabilitation Medicine Yenimahalle Research Hospital Yıldırım Beyazıt University Ankara Turkey Department of Physical and Rehabilitation Medicine Hacettepe University Medical School Ankara Turkey

Recent studies showed that fine motor control dysfunction was observed in fibromyalgia (FM) syndrome as well as allodynia. However, brain signatures of this association still remain unclear. In this study, finger tapping task (FTT) and median nerve stimulation (MNS) were applied to both hands of 15 FM patients and healthy controls (HC) to understand this relationship. Hemodynamic activity was measured simultaneously using functional near-infrared spectroscopy (fNIRS). Experiments were analyzed separately by using 2x2 repeated measures ANOVA. Results for the FTT experiment revealed that HC showed higher activity than FM patients in bilateral superior parietal gyrus (SPG), left supramarginal gyrus (SMG) and right somatosensory cortex (SI). Furthermore, right-hand FTT resulted in higher activity than left-hand FTT in left SPG, left SI and right motor cortex (MI). In the MNS experiment, FM patients showed higher activity than HC in bilateral SPG, right SMG, right SI and right middle frontal gyrus (MFG). Negative correlation was observed in left SPG between FTT and MNS activities. Besides, MNS activity in left SPG was negatively correlated with left-hand pain *** study revealed that left SPG might be an important indicator to associate fine motor loss and allodynia in FM.

关键词： fNIRS Fibromyalgia Fine Motor Loss

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Community-based intervention in reducing flood impacts in Gambia

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IOP Conference Series: Earth and Environmental Science 2021年第1期711卷

作者： A Jonga E Meilianda Nizamuddin Graduate Program School of Disaster Science Universitas Syiah Kuala Banda Aceh 23111 Indonesia Tsunami and Disaster Mitigation Research Centre (TDMRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia Civil Engineering Department Engineering Faculty Universitas Syiah Kuala Banda Aceh 23111 Indonesia Informatics Department Mathematics and Natural Science Faculty Universitas Syiah Kuala Banda Aceh 23111 Indonesia

Flood has caused significant damage to the agricultural sector, which results in low productivity and causes famine in Kaba-kama community of Basse in the Upper river region of the Gambia. This study analyses the potential role of the community's mitigation efforts to reduce the flood impact in the Gambia. The objective is to determine the influence of community-based intervention activities in reducing the flood impact at the community level. The primary data are obtained in this study from own previous experience by participating in the community work ('Te-setto') to mitigate the impact of flood, and interviews in a form of questionnaires to the members of the VDC in Kaba-kama community. Literature reviews from journals, books, and the final report about the disaster in 2018 by NDMA has served as a secondary source of data for the study. Notifiable, the community-based organization (Village Development Committee -VDC), is the key pioneer of the initiative. This study exemplifies the successful collective efforts by the communities and government in different regions and the country at large in practicing flood adaptive and mitigation strategies, which has potential to be implemented elsewhere in other famine-prone countries in the world.

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Joint Observation of the Galactic Center with MAGIC and CTA-LST-1 38

Joint Observation of the Galactic Center with MAGIC and CTA-...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Abe, Shotaro Berti, Alessio Di Pierro, Federico López-Coto, Rubén Moralejo, Abelardo Ohtani, Yoshiki Sitarek, Julian Strzys, Marcel Suda, Yusuke Vovk, Ievgen Abe, H. Abe, K. Abe, S. Abhir, J. Acciari, V.A. Aguasca-Cabot, A. Agudo, I. Alvarez Crespo, N. Aniello, T. Ansoldi, S. Antonelli, L.A. Aramo, C. Arbet-Engels, A. Cornelia, A. Artero, M. Asano, K. Aubert, P. Baack, D. Babić, A. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Baxter, J. Becerra González, J. Bednarek, W. Bernardini, E. Bernardos, M.I. Bernete Medrano, J. Berti, A. Besenrieder, J. Bhattacharjee, P. Biederbeck, N. Bigongiari, C. Biland, A. Bissaldi, E. Blanch, O. Bonnoli, G. Bordas, P. Bošnjak, Ž. Bulgarelli, A. Burelli, I. Burmistrov, L. Buscemi, M. Busetto, G. Campoy-Ordaz, A. Cardillo, M. Caroff, S. Carosi, A. Carosi, R. Carrasco, M.S. Carretero-Castrillo, M. Cassol, F. Castro-Tirado, A.J. Cauz, D. Cerasole, D. Ceribella, G. Chai, Y. Cheng, K. Chiavassa, A. Chikawa, M. Chilingarian, A. Chytka, L. Cifuentes, A. Cikota, S. Colombo, E. Contreras, J.L. Cortina, J. Costantini, H. Covino, S. D’Amico, G. Dalchenko, M. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. de Bony de Lavergne, M. De Lotto, B. De Lucia, M. de Menezes, R. Del Peral, L. Deleglise, G. Delfino, M. Delgado, C. Delgado Mengual, J. della Volpe, D. Dellaiera, M. Del Popolo, A. Depaoli, D. Di Piano, A. Di Pierro, F. Di Pilato, A. Di Tria, R. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Eisenberger, L. Elsässer, D. Emery, G. Escudero, J. Fallah Ramazani, V. Fariña, L. Fattorini, A. Ferrara, G. Ferrarotto, F. Fiasson, A. Foffano, L. Font, L. Freixas Coromina, L. Fröse, S. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Garczarczyk, M. Gasbarra, C. Gasparrini, D. Gasparyan, S. Gaug, M. Geyer, D. Giesbrecht Paiva, J. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Grau, R. Green, J. Green, D. Gunji, S. Günther, P. Hackfeld, J. Hadasch, D. Hahn, A. Institute for Cosmic Ray Research Kashiwa-no-ha 5-1-5 Kashiwa-shi Chiba Japan Max-Planck-Institut für Physik Föhringer Ring 6 München80805 Germany INFN Sezione di Torino Via P. Giuria 1 Torino10125 Italy Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Faculty of Physics and Applied Informatics University of Lodz ul. Pomorska 149-153 Lodz90-236 Poland Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Institute for Cosmic Ray Research University of Tokyo Japan Department of Physics Tokai University Japan ETH Zürich Switzerland Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain EMFTEL department IPARCOS Universidad Complutense de Madrid Spain Italy INFN Sezione di Trieste Università degli Studi di Udine Italy Italy INAF Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy INFN Sezione di Padova Università degli Studi di Padova Italy LAPP Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS-IN2P3 Annecy France Department of Physics TU Dortmund University Germany Croatia Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan Universidad del Azuay Ecuador INAF Osservatorio di Astrofisica e Scienza dello spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil CIEMAT Spain INFN Sezione di Bari Politecnico di Bari Italy INAF Osservatorio Astronomico di Brera Italy University of Geneva Département de physique nucléaire et corpusculaire Switzerland INFN Sezione di Catania Italy Departament de Física CERES-IEEC Universitat Autónoma de Barcelona Spain Italy Università di Pisa INFN Pisa Italy Aix Mars

MAGIC is a system of two Imaging Atmospheric Cherenkov Telescopes (IACTs), designed to detect very-high-energy gamma rays, and is operating in stereoscopic mode since 2009 at the Observatorio del Roque de Los Muchachos in La Palma, Spain. In 2018, the prototype IACT of the Large-Sized Telescope (LST-1) for the Cherenkov Telescope Array, a next-generation ground-based gamma-ray observatory, was inaugurated at the same site, at a distance of approximately 100 meters from the MAGIC telescopes. Using joint observations between MAGIC and LST-1, we developed a dedicated analysis pipeline and established the threefold telescope system via software, achieving the highest sensitivity in the northern hemisphere. Based on this enhanced performance, MAGIC and LST-1 have been jointly and regularly observing the Galactic Center, a region of paramount importance and complexity for IACTs. In particular, the gamma-ray emission from the dynamical center of the Milky Way is under debate. Although previous measurements suggested that a supermassive black hole Sagittarius A* plays a primary role, its radiation mechanism remains unclear, mainly due to limited angular resolution and sensitivity. The enhanced sensitivity in our novel approach is thus expected to provide new insights into the question. We here present the current status of the data analysis for the Galactic Center joint MAGIC and LST-1 observations. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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Success Factors of the Blockchain Adoption for Smart Manufacture

Success Factors of the Blockchain Adoption for Smart Manufac...

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2018 International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2018

作者： Surjandy Fernando, Erick Meyliana Kosala, Raymond Warnars, Harco Leslie Hendric Spits Abdurachman, Edi Supangkat, Suhono Harso Computer Science Department BINUS Graduate Program - Doctor of Computer Science Information Systems Audit Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781538674222

Currently, the discussion about smart city becomes a trend topic in which the development of the Internet of Thing (IoT) is used in various sectors including in the manufacturing sector where the Indonesian government is intensively increasing the capacity and production quality. The use of IoT in both smart manufacture and smart city become an interesting thing topic for research and the combination of the new technology Blockchain, may proof the reliability of data. Trust, transparent, secure, real time online process as a new phenomenon to merge IoT with Blockchain technology into smart manufacturing technology. However, for maintaining quality and sustainability, required technology governance so the achievement of implementation can be done well. This research uses the method called Systematic Literature Review (SLR) find the Blockchain adoption factors for Smart Manufacturing Technology Governance and the research conducted on several reputable publications. The result found, there are 35 adoption success factors found from the adoption of blockchain technology into smart manufacture industry. The influence of the technology governance process in smart manufacture industry increase sustainability, realtime and accurate decision making for the industry. © 2018 IEEE.

关键词： Manufacture

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Developing self-adaptive microservice systems: Challenges and directions

arXiv

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arXiv 2019年

作者： Mendonça, Nabor C. Jamshidi, Pooyan Garlan, David Pahl, Claus Post Graduate Program in Applied Informatics University of Fortaleza FortalezaCE Brazil Computer Science and Engineering Department University of South Carolina ColumbiaSC United States School of Computer Science Carnegie Mellon University PittsburghPA United States Faculty of Computer Science Free University of Bozen-Bolzano Bozen-Bolzano Italy

A self-adaptive system can dynamically monitor and adapt its behavior to preserve or enhance its quality attributes under uncertain operating conditions. This article identifies key challenges for the development of microservice applications as self-adaptive systems, using a cloud-based intelligent video surveillance application as a motivating example. It also suggests potential new directions for addressing most of the identified challenges by leveraging existing microservice practices and technologies. Copyright © 2019, The Authors. All rights reserved.

关键词： Adaptive systems

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