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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Ryosuke Suzuki Tadachika Ozono Toramatsu Shintani Department of Computer Science Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

The diffusion of the Augmented Reality framework has facilitated the implementation of a support system for a real world task. This paper introduces a system that supports the Mahjong game for beginners. However, it is difficult to calculate the score for beginners in the real Mahjong game. We develop an offline system to get the score by recognizing the Mahjong tiles. This is a difficult task because Mahjong tiles have classes and attributes that are the context of their positions. The system needs context-aware image recognition. The system detected tiles using OpenCV and Convolutional Neural Network to classify them and self-localization indirectly found in attributes. The experimental results show that the system can be used effectively.

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Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications

arXiv

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arXiv 2021年

作者： Giannakopoulos, Ilias I. Guryev, Georgy D. Serrallés, José E.C. Georgakis, Ioannis P. Daniel, Luca White, Jacob K. Lattanzi, Riccardo Department of Radiology New York University Grossman School of Medicine NY United States Research Laboratory of Electronics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Center for Biomedical Imaging Department of Radiology New York University Grossman School of Medicine NY United States Vilcek Institute of Graduate Biomedical Sciences New York University Grossman School of Medicine NY United States

—In this work, we propose a method for the compression of the coupling matrix in volume-surface integral equation (VSIE) formulations. VSIE methods are used for electromagnetic analysis in magnetic resonance imaging (MRI) applications, for which the coupling matrix models the interactions between the coil and the body. We showed that these effects can be represented as independent interactions between remote elements in 3D tensor formats, and subsequently decomposed with the Tucker model. Our method can work in tandem with the adaptive cross approximation technique to provide fast solutions of VSIE problems. We demonstrated that our compression approaches can enable the use of VSIE matrices of prohibitive memory requirements, by allowing the effective use of modern graphical processing units (GPUs) to accelerate the arising matrix-vector products. This is critical to enable numerical MRI simulations at clinical voxel resolutions in a feasible computation time. In this paper, we demonstrate that the VSIE matrix-vector products needed to calculate the electromagnetic field produced by an MRI coil inside a numerical body model with 1 mm3 voxel resolution, could be performed in ∼ 33 seconds in a GPU, after compressing the associated coupling matrix from ∼ 80 TB to ∼ 43 MB. © 2021, CC BY.

关键词： Graphics processing unit

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Sensing the local magnetic environment through optically active defects in a layered magnetic semiconductor

arXiv

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arXiv 2022年

作者： Klein, Julian Song, Zhigang Pingault, Benjamin Dirnberger, Florian Chi, Hang Curtis, Jonathan B. Dana, Rami Bushati, Rezlind Quan, Jiamin Dekanovsky, Lukas Sofer, Zdenek Alù, Andrea Menon, Vinod M. Moodera, Jagadeesh S. Lončar, Marko Narang, Prineha Ross, Frances M. Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States College of Letters and Sciences UCLA Los AngelesCA90095 United States QuTech Delft University of Technology Delft2600 GA Netherlands Department of Physics City College of New York New YorkNY10031 United States Francis Bitter Magnet Laboratory Plasma Science and Fusion Center Massachusetts Institute of Technology CambridgeMA02139 United States U.S. Army CCDC Army Research Laboratory AdelphiMD20783 United States Department of Physics The Graduate Center City University of New York New YorkNY10016 United States Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Photonics Initiative CUNY Advanced Science Research Center New YorkNY10031 United States Department of Electrical Engineering City College of the City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10026 United States Department of Inorganic Chemistry University of Chemistry and Technology Prague Technická 5 Prague 6166 28 Czech Republic Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States

Atomic-level defects in van der Waals (vdW) materials are essential building blocks for quantum technologies and quantum sensing applications. The layered magnetic semiconductor CrSBr is an outstanding candidate for exploring optically active defects owing to a direct gap in addition to a rich magnetic phase diagram including a recently hypothesized defect-induced magnetic order at low temperature. Here, we show optically active defects in CrSBr that are probes of the local magnetic environment. We observe spectrally narrow (1 meV) defect emission in CrSBr that is correlated with both the bulk magnetic order and an additional low temperature defect-induced magnetic order. We elucidate the origin of this magnetic order in the context of local and non-local exchange coupling effects. Our work establishes vdW magnets like CrSBr as an exceptional platform to optically study defects that are correlated with the magnetic lattice. We anticipate that controlled defect creation allows for tailor-made complex magnetic textures and phases with the unique ingredient of direct optical access. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic semiconductors

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The Role of Animal-Assisted Therapy in Enhancing Patients' Well-Being: Systematic Study of the Qualitative and Quantitative Evidence

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JMIRx med 2024年 5卷 e51787页

作者： Ramendra Pati Pandey Riya Mukherjee Chung-Ming Chang School of Health Sciences & Technology University of Petroleum and Energy Studies Dehradun Uttarakhand India. Graduate Institute of Biomedical Sciences Chang Gung University Taoyuan Taiwan. Master & PhD Program in Biotechnology Industry Chang Gung University Taoyuan Taiwan. Department of Medical Biotechnology and Laboratory Science Chang Gung University Taoyuan Taiwan.

Background:Animal-assisted therapy, also known as pet therapy, is a therapeutic intervention that involves animals to enhance the well-being of individuals across various populations and settings. Objective:This systematic study aims to assess the outcomes of animal-assisted therapy interventions and explore the associated policies. Methods:A total of 16 papers published between 2015 and 2023 were selected for analysis. These papers were chosen based on their relevance to the research topic of animal-assisted therapy and their availability in scholarly databases. Thematic synthesis and meta-analysis were used to synthesize the qualitative and quantitative data extracted from the selected papers. Results:The analysis included 16 studies that met the inclusion criteria and were deemed to be of moderate or higher quality. Among these studies, 4 demonstrated positive results for therapeutic mediation and one for supportive mediation in psychiatric disorders. Additionally, all studies showed positive outcomes for depression and neurological disorders. Regarding stress and anxiety, 3 studies indicated supportive mediation, while 2 studies showed activating mediation. Conclusions:The overall assessment of animal-assisted therapy shows promise as an effective intervention in promoting well-being among diverse populations. Further research and the establishment of standardized outcome assessment measures and comprehensive policies are essential for advancing the field and maximizing the benefits of animal-assisted therapy.

关键词： animal-assisted therapy outcome assessment pet therapy policies systematic study

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Neighborhood mutual remainder: Self-stabilizing implementation of look-compute-move robots

arXiv

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arXiv 2019年

作者： Dolev, Shlomi Kamei, Sayaka Katayama, Yoshiaki Ooshita, Fukuhito Wada, Koichi Department of Computer Science Ben-Gurion University of the Negev Israel Department of Information Engineering Graduate School of Engineering Hiroshima University Japan Department of Computer Science and Engineering Graduate School of Engineering Nagoya Institute of Technology Japan Graduate School of Science and Technology Nara Institute of Science and Technology Japan Department of Applied Informatics Faculty of Science and Engineering Hosei University Japan

Local mutual exclusion guarantees that no two neighboring processes enter a critical section at the same time while satisfying both mutual exclusion and no starvation properties. On the other hand, processes may want to execute some operation simultaneously with the neighbors. Of course, we can use a globally synchronized clock to achieve the task but it is very expensive to realize it in a distributed system in general. In this paper, we define a new concept neighborhood mutual remainder. A distributed algorithm that satisfies the neighborhood mutual remainder requirement should satisfy global fairness, l-exclusion and repeated local rendezvous requirements. Global fairness is satisfied when each process (that requests to enter the critical section infinitely often) executes the critical section infinitely often, l-exclusion is satisfied when at most l neighboring processes enter the critical section at the same time, and repeated local rendezvous is satisfied when for each process infinitely often no process in the closed neighborhood is in the critical or trying section. We first formalize the concept of neighborhood mutual remainder, and give a simple self-stabilizing algorithm to demonstrate the design paradigm to achieve neighborhood mutual remainder. We also present two applications of neighborhood mutual remainder to a Look-Compute-Move robot system. One is for implementing a move-atomic property and the other is for implementing FSYNC scheduler, where robots possess an independent clock that is advanced in the same speed. These are the first self-stabilizing implementations of the LCM synchronization. Copyright © 2019, The Authors. All rights reserved.

关键词： Clocks

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Detection of Vehicle Position and Speed using Camera Calibration and Image Projection Methods

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Procedia computer science 2019年 157卷 255-265页

作者： Alexander A S Gunawan Deasy Aprilia Tanjung Fergyanto E. Gunawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480

Traffic congestion is the main problem faced by big cities, such as Jakarta. One approach to reduce congestion levels is to improve traffic management that regulates and controls the number of vehicles. To evaluate the impact of traffic management before direct implementation on the highway, traffic modeling can be carried out. Parameters in modeling traffic must be determined from a calibration process where the vehicle is accurately measured for its position and speed. This study aims to propose an efficient calibration procedure with accurate results, based on recorded vehicle movement in perspective view. First, the road image is projected using the Direct Linear Transformation (DLT) method, then the vehicle position is detected using the Background Subtraction and tracked using Mixture of Gaussian (MoG) to determine the vehicle speed. Finally, we develop a prototype of Automated Traffic Flow Monitoring based on Python programming. In the experiment results, the accuracy of vehicle position detection is evaluated based on the Euclidean distance. The average difference between the results of position detection with ground-truth is 12.07 pixels with a camera angle 40 °. The percentage of speed measurement accuracy using the DLT projection method is 96.14%.

关键词： Music recommender system Convolutional recurrent neural network Similarity distance

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Analysis of Gamification Models in Education Using MDA Framework 3

Analysis of Gamification Models in Education Using MDA Frame...

引用

3rd International Conference on computer science and Computational Intelligence, ICCSCI 2018

作者： Kusuma, Gede Putra Wigati, Evan Kristia Utomo, Yesun Putera Suryapranata, Louis Khrisna Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Gamification nowadays is being one of techniques that can increase motivation and encourage the involvement of users, particularly in education domain that requires teaching and learning activities to be more fun and interesting. This paper surveys some analysis of gamification models. MDA framework is used to identify surveyed papers by breaking them down into three categories: mechanics, dynamics and aesthetics. Findings from the survey show there are many gamification models in education domain. However, there are some very representative gamification models could be used as a method to increase motivation, achievement and engagement in learning activities. By knowing the latest gamification models in education domain stated in this paper, it could help gamification practitioners to make new strategies in learning activities to increase students' motivation, achievement and involvement. We also suggest some gamification strategies, which combine several mechanics in such a way to create dynamics that results in all types of aesthetics outputs. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Students

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An Evaluation of Deep Neural Network Performance on Limited Protein Phosphorylation Site Prediction Data

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Procedia computer science 2019年 157卷 25-30页

作者： Favorisen Rosyking Lumbanraja Bharuno Mahesworo Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Department of Computer Science Faculty of Mathematics and Natural Science University of Lampung Jalan Prof. Dr. Sumantri Brojonegoro No.17 35145 Bandar Lampung Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

One of the common and important post-translational modification (PTM) types is phosphorylation. Protein phosphorylation is used to regulate various enzyme and receptor activations which include signal pathways. There have been many significant studies conducted to predict phosphorylation sites using various machine learning methods. Recently, several researchers claimed deep learning based methods as the best methods for phosphorylation sited prediction. However, the performance of these methods were backed up with the massive training data used in the researches. In this paper, we study the performance of simple deep neural network on the limited data generally used prior to deep learning employment. The result shows that a deep neural network can still achieve comparable performance in the limited data settings.

关键词： Phosphorylation Site Prediction Protein Phosphorylation Deep Learning Deep Neural Network

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引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

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