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International Conference on Computer Supported Cooperative Work in Design

作者： Gregory Moro Puppi Wanderley Marie-Hélène Abel Emerson Cabrera Paraiso CNRS Université de Technologie de Compiègne Compiègne France Graduate Program in Informatics Pontifícia Universidade Católica do ParanáPPGIa Curitiba Brazil

ISBN: (纸本)9781538614839

Cooperation is a fundamental trait of Systems of Systems (SoS). In this paper we discuss the design and implementation of proactive vocal interfaces with the goal of improving and stimulating cooperation between users and constituent systems. Our approach is based on intelligent personal assistants displaying proactive behavior and capable of interacting vocally. The paper discusses the main challenges for integrating proactive behavior into SoS interfaces, as well as for using them. The proposed approach is generic and can be applied to different domains.

关键词： Browsers Collaboration System of systems Ontologies Software Natural languages Computer architecture

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Rainfall forecasting using PSPline and rice production with ocean-atmosphere interaction 2

Rainfall forecasting using PSPline and rice production with ...

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2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Caraka, Rezzy Eko Supatmanto, Budi Darmawan Tahmid, Muhammad Soebagyo, Joko Mauludin, M. Ali Iskandar, Akbar Pardamean, Bens Bina Nusantara University Jakarta11480 Indonesia Indonesia Indonesia Agency for Meteorology Climatology and Geophysics Indonesia College of Technology Wastukancana Purwakarta Indonesia Laboratory of Sociology and Extension Padjadjaran University Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The role of climate can be affected by plants. The weather can accelerate and multiply the existence of various plant pests and diseases, accelerate the growth and development of grass among plants, and encourage the emergence of infection and significant damage to plants. The elements of climate that affect the growth of plants are one of them is rainfall. In this paper, we performed the simulation using the non-parametric penalized spline (PSPLINE) method and studied the effect on rice production in Lampung. It can be concluded that the increasing fluctuation, frequency, and intensity of climate anomalies in the last decade caused by the ENSO phenomenon have an impact on changes in distribution patterns, intensity, and period of the wet season so that the start of the rainy season and the dry season becomes too late. As a result, there is a seasonal shift from normal average conditions that can ultimately have severe implications for food crops. In a nutshell penalized spline gives high accuracy with R 2 = 96.227% and MAPE = 1.62%. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Rain

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Training of Physical Neural Networks

arXiv

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Smart mathematics: a kindergarten student learning media based on the drill and practice model

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Journal of Physics: Conference Series 2019年第1期1175卷 012037-012037页

作者： Dwi Ely Kurniawan Widyastuti, Hilda Dzikri, Afdhol Sembiring, Evaliata Manurung, Rosida Tiurma Department of Informatics Engineering Politeknik Negeri Batam Department of Multimedia and Network Engineering Politeknik Negeri Batam Graduate Program in Scientific Psychology Universitas Kristen Maranatha

Teachers still face difficulties in catching up to new teaching methods, especially the kindergarten teachers. The problem is their inadequate application of relevant advanced learning media technology. This should have been done by integrating learning and playing, in an enjoyable atmosphere, to stimulate the creativity of children. This research aimed to enhance kindergarten cognitive ability of children in understanding general knowledge, general science, and the concepts of shape, pattern, size, and color using the drill and practice model, and identify effectiveness application of interactive multimedia on the learning outcome of students. This development of interactive multimedia application was storyboard-driven and employed the Hannafin and Peck methodology. During the research, 30 Islamic kindergarten student all over Batam was involved. The interactive multimedia application evaluation included pre-test and post-test. The data from these tests were then analyzed using the quantitative descriptive method. The evaluation consisted of the functional and significance test. Based on the pre-test and post-test results, the significance test resulted in Sig. (2-tailed) < 0.05, which indicated the effectiveness of the interactive multimedia application.

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A Blockchain Approach for the Future Renewable Energy Transaction

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Journal of Physics: Conference Series 2019年第1期1175卷

作者： Sandi Rahmadika Diena Rauda Ramdania Maisevli Harika Interdisciplinary Program of Information Security Graduate School PKNU Pukyong National University Busan South Korea Informatics Engineering UIN Sunan Gunung Djati Bandung Indonesia Computer Engineering and Informatics Politeknik Negeri Bandung Indonesia

Blockchain technology holds great promise to rewire the current financial system which relies on the third party. Every transaction is recorded in a secure and transparent for the parties in the blockchain network. Blockchain technology in the energy sector becomes an interesting topic among researchers at this moment. The use of blockchain allows producers and consumers to trade energy transactions through smart grids because of a decentralized energy trading system. The trading activities without a third party involved would reduce the cost of a transaction thus it brings to a new level of quality of service in the trading system. In this paper, we propose an architectural model for decentralized energy trading system among the neighbors that allows the producer who has the surplus energy to conduct a trading activity with his/her neighbors. The transactions manage by the miners in the same blockchain network. Moreover, we analyze the security issues from various attacks and presenting the performance of the selected attack that might occur in the model.

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Information hiding based on histogram and pixel pattern

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Journal of Cyber Security and Mobility 2017年第4期6卷 397-425页

作者： Barmawi, Ari Moesriami Pradeka, Deden Informatics Graduate Program School of Computing Telkom University Indonesia

Recently, information exchange using internet is increasing, such that information security is necessary for securing confidential information because it is possible to eavesdrop the information. There are several methods for securing the exchanged information such as was proposed by Rejani et al. Rejani's method can be noiseless in low capacity but noisy in high capacity. In the case of high capacity, it will raise suspicion. This research proposed a method based on histogram and pixel pattern for keeping the stego image noiseless while still keeping the capacity high. Secret information can be embedded into the cover by evaluating the histogram and map the characters used in the secret message to the consecutive intensity in the cover image histogram. The map of the characters is sent to the recipient securely. Using the proposed method there is no pixel value changes during the embedding process. Based on the result of the experiments, it is shown that in noiseless condition, the proposed method has higher embedding capacity than Rejani's especially when using cover image with sizes larger than 128 × 128. Thus, in noiseless condition the embedding capacity using the proposed method is higher than Rejani's method in noiseless condition. © 2018 the Author(s).

关键词： Graphic methods

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Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study

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The Lancet Digital Health 2022年第6期4卷 e455-e465页

作者： Lo-Ciganic, Wei-Hsuan Donohue, Julie M Yang, Qingnan Huang, James L Chang, Ching-Yuan Weiss, Jeremy C Guo, Jingchuan Zhang, Hao H Cochran, Gerald Gordon, Adam J Malone, Daniel C Kwoh, Chian K Wilson, Debbie L Kuza, Courtney C Gellad, Walid F Department of Pharmaceutical Outcomes and Policy College of Pharmacy University of Florida Gainesville FL United States Center for Drug Evaluation and Safety College of Pharmacy University of Florida Gainesville FL United States Department of Health Policy and Management Graduate School of Public Health University of Pittsburgh Pittsburgh PA United States Center for Pharmaceutical Policy and Prescribing Health Policy Institute University of Pittsburgh Pittsburgh PA United States Division of General Internal Medicine School of Medicine University of Pittsburgh Pittsburgh PA United States Heinz College of Information Systems and Public Policy Carnegie Mellon University Pittsburgh PA United States Department of Mathematics University of Arizona Tucson AZ United States Division of Rheumatology Department of Medicine University of Arizona Arthritis Center University of Arizona Tucson AZ United States Program for Addiction Research Clinical Care Knowledge and Advocacy Division of Epidemiology Department of Internal Medicine University of Utah Salt Lake City UT United States Department of Pharmacotherapy College of Pharmacy University of Utah Salt Lake City UT United States Informatics Decision-Enhancement and Analytic Sciences Center Veterans Affairs Salt Lake City Health Care System Salt Lake City UT United States Center for Health Equity Research Promotion Veterans Affairs Pittsburgh Healthcare System Pittsburgh PA United States

Background: Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state). Methods: This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013–16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017–18 Pennsylvania Medicaid data (n=318 585) and (2) 2015–17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use. Findings: A total of 8641 (1·35%) 2013–16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017–18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015–17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013–16 Pennsylvania training dataset and validated on the 2013–16 Pennsylvania internal validation dataset, 2017–18 Pennsylvania external validation dataset, and 2015–17 Arizona external validation dataset were 0·841 (95%

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Generation of terahertz transients from Co2Fe0.4Mn0.6Si-Heusler-alloy/normal-metal nanobilayers excited by femtosecond optical pulses

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Physical Review Research 2021年第4期3卷 043025-043025页

作者： Sarah Heidtfeld Roman Adam Takahide Kubota Koki Takanashi Derang Cao Carolin Schmitz-Antoniak Daniel E. Bürgler Fangzhou Wang Christian Greb Genyu Chen Ivan Komissarov Hilde Hardtdegen Martin Mikulics Roman Sobolewski Shigemasa Suga Claus M. Schneider Research Centre Jülich Peter Grünberg Institute 52425 Jülich Germany Faculty of Physics University of Duisburg-Essen 47048 Duisburg Germany Institute for Materials Research Tohoku University Sendai 980-8577 Japan Center for Spintronics Research Network Tohoku University Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (Core Research Cluster) Tohoku University Sendai 980-8577 Japan College of Physics National Demonstration Center for Experimental Applied Physics Education Qingdao University Qingdao 266071 China Materials Science Graduate Program University of Rochester Rochester New York 14627 USA Laboratory for Laser Energetics University of Rochester Rochester New York 14623 USA Department of Electrical and Computer Engineering University of Rochester Rochester New York 14627 USA Belarusian State University of Informatics and Radioelectronics Minsk 220013 Belarus Research Centre Jülich Ernst Ruska Centre for Microscopy and Spectroscopy with Electrons 52425 Jülich Germany Department of Physics and Astronomy University of Rochester Rochester New York 14627 USA The Institute of Scientific and Industrial Research Osaka University Ibaraki Osaka 567-0047 Japan Department of Physics University of California Davis Davis California 95616 USA

We generated pulses of electromagnetic radiation in the terahertz (THz) frequency range by optical excitation of Co2Fe0.4Mn0.6Si (CFMS)/normal-metal (NM) bilayer structures. The CFMS is a Heusler alloy showing a band gap in one spin channel and is therefore a half metal. We compared the THz emission efficiency in a systematic manner for four different CFMS/NM bilayers, where NM was either Pt, Ta, Cr, or Al. Our measurements show that the THz intensity is highest for a Pt capping. We also demonstrate the tunability of the THz amplitude by varying the magnetic field for all four bilayers. We attribute the THz generation to the inverse spin Hall effect. In order to investigate the role of the interface in THz generation, we measured the spin mixing conductance for each CFMS/NM bilayer using a ferromagnetic resonance method. We found that the spin-orbit coupling cannot completely describe the THz generation in the bilayers and that the spin transmission efficiency of the CFMS/NM interface and the spin diffusion length, as well as the oxidation of the NM layer, play crucial roles in the THz emission process.

关键词： Inverse spin Hall effect Magnetism Spin injection Spin pumping Spintronics Ferromagnetic resonance Terahertz time-domain spectroscopy

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CCTV traffic congestion analysis at Pejompongan using case based reasoning 1

CCTV traffic congestion analysis at Pejompongan using case b...

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1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Surjandy Anindra, Firman Soeparno, Haryono Napitupulu, Togar Alam Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Departement BINUS Graduate Program - Master of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Departement of Informatics and Communication Universitas Nasional Jakarta12520 Indonesia Information Systems Department BINUS Graduate Program - Master of Information Systems Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538609545

Congestion in Jakarta is a chronic problem and is still being sought. The growing city of Jakarta is facing various international events, increasing the complexity of these congestion problems. This research is aimed to propose an alternative solution for congestion problem in Jakarta using Cased-Based Reasoning (CBR) approach on Pejompongan area. The CBR integrated model analyzes congestion at a given point in a particular hour to identify the same case. The Cased-Based Management (CBM) algorithm approach is used to capture transient problems and ultimately reduce case base cases for their contribution to long-term maintenance. Congestion data is obtained from CCTV cameras via website *** in some strategic areas. With that data, we use CBR's integrated model to adjust traffic time for traffic flow to be optimized so that congestion will decompose. The results of this study can be used by relevant government agencies and policymakers in Jakarta to formulate the best solution to solve the congestion problem in the Indonesian capital. Image analysis at nights and bad weather conditions become a limitation and also the integration with traffic lights control in this paper that can be a new topic for further research. © 2018 IEEE.

关键词： Traffic congestion

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High-Throughput Computational Studies in Catalysis and Materials Research, and their Impact on Rational Design

arXiv

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arXiv 2019年

作者： Afzal, Mohammad Atif Faiz Hachmann, Johannes Schrödinger Inc. PortlandOR97204 United States Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo The State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

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