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IEEE International Conference on Electronics, Circuits and Systems

作者： Luciano Almeida Braatz Antonio Carlos Schneider Beck Bruno Zatt Luciano Volcan Agostini Daniel Munari Palomino Marcelo Schiavon Porto Graduate Program in Computer Science (PPGC) Video Technology Research Group (ViTech) Federal University of Pelotas (UFPel) Brazil Institute of Informatics (INF) Federal University of Rio Grande do Sul (UFRGS) Brazil

This paper presents a hardware-friendly algorithm to maximize the throughput of the Discrete Cosine Transform (DCT) of the High Efficiency Video Coding (HEVC), together with its hardware design. The Fast DCT (FCT) algorithm is based on the Cooley-Tuckey algorithm for the Fast Fourier Transform (FFT) with pre- and post-processing required to maintain the compliance with the HEVC. The resulting algorithm allows high throughput while maintaining low power dissipation. The designed hardware was synthesized for a 45-nm Nangate technology and it reaches a throughput of 81.28GSamples per second when consuming 12.33mW. Such energy efficiency and throughput surpass all related works in the literature.

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Mixture-of-experts variational autoencoder for clustering and generating from similarity-based representations

arXiv

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arXiv 2019年

作者： Kopf, Andreas Fortuin, Vincent Somnath, Vignesh Ram Claassen, Manfred Institute of Molecular Systems Biology Department of Biology ETH Zürich Switzerland Biomedical Informatics Group Department of Computer Science ETH Zürich Switzerland Switzerland Life Science Graduate School Zurich PhD Program Systems Biology Switzerland

Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering gained popularity due to the non-linearity of neural networks, which allows for flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on synthetic data, the MNIST benchmark data set, and a challenging real-world task of defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines and we show that the MoE architecture in the decoder reduces the computational cost of sampling specific data modes with high fidelity. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Application of factor analysis method to support the users acceptance model of ERP systems implementation

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Journal of Physics: Conference Series 2021年第1期1836卷

作者： W Sardjono J Sudirwan W Priatna G R Putra Information Systems Management Department BINUS Graduate Program -Master of Information Systems Management Bina Nusantara University Jakarta Indonesia – 11480 School of Information Systems Department BINUS Undergraduate Program Bina Nusantara University Jakarta Indonesia 11480 Informatics Engineering Engineering Bhayangkara University Jakarta Raya Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

An e-Commerce company has been using an Enterprise Resource Planning (ERP) system for several years, but is still constrained in its implementation, this is reflected in the number of issue/change request tickets submitted by users who have not been able to reach ideal conditions. To overcome this problem, we need an appropriate reference model, the ERP system user acceptance model in companies. The study conducted factors that influence the level of acceptance of SAP ERP users in the XYZ group to then determine a model that is able to explain practically the level of ERP system user acceptance, which in turn can produce concrete proposals to increase ERP system user acceptance. Research is quantitative descriptive using factor analysis and regression analysis methods. The research method uses a questionnaire with a total of 107 valid respondents coming from 8 company names under the XYZ group in 6 Southeast Asian countries. The conclusion of the study is that there are 24 indicators and 4 factors that influence user acceptance of the SAP ERP system. These four factors are the low contribution of the ERP system, the quality of ERP systems and data, the development of ERP systems, and also the low quality of ERP system supporters. It is hoped that by optimizing the value of the 4 factors, the XYZ group can increase user acceptance of the SAP ERP system so that the system can provide benefits expected by the company to be competitive advantage.

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Revised JNLPBA corpus: A revised version of biomedical NER corpus for relation extraction task

arXiv

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arXiv 2019年

作者： Huang, Ming-Siang Lai, Po-Ting Tsai, Richard Tzong-Han Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang Ming University Taipei Taiwan Department of Computer Science National Tsing-Hua University Hsinchu Taiwan Intelligent Agent Systems Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Intelligent Information Service Research Laboratory Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task. We preserve the original entity types including protein, DNA, RNA, cell line and cell type while all the abstracts in JNLPBA corpus are manually curated by domain experts again basis on the new annotation guideline focusing on the specific NEs instead of general terms. Simultaneously, several imperfection issues in JNLPBA are pointed out and made up in the new corpus. To compare the adaptability of different NER systems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured in three open sources NER systems including BANNER, Gimli and NERSuite. In the same circumstance, all the systems perform average 10% better in Revised JNLPBA than in JNLPBA. Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application. The revised JNLPBA corpus is freely available at ***/BNER/Content/Revised_***. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

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Cloud computing model in higher education

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IOP Conference Series: Materials science and Engineering 2020年第1期852卷

作者： Bagus Mulyawan Raymond Kosala Benny Ranti Suhono Harso Supangkat Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Universitas Tarumanagara Jakarta 11440 Computer Science Department Faculty of Computer Science Universitas Indonesia Indonesia 16424 Schoolof Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

Currently, higher education is institutions that should be managed as a global business institution. Higher education should improve continually its business processes according to the demands of increasing its stakeholders. of the problems faced in the development of educational, activities if related to the use of information technology for each service in higher education for stakeholders is the issue of available funds. Cloud computing also has advantages related to infrastructure and maintenance and increases operating efficiency. Most institutions are currently moving towards cloud computing technology to reduce operational costs. For achieving these objectives, the institutions need to be supported by reliable technology. This study aim to propose cloud model for education and IT Governance conceptual framework for Cloud Computing model adoption, Tarumanagara University as a study case.

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Implementation of Mobile game for Religion Learning

Implementation of Mobile game for Religion Learning

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IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Edy Sulistiyo Vindy Nizirwan Anwar Diana Teresia Spits Warnars Setya Chendra Wibawa Goenawan Brotosaputro Dedy Prasetya Kristiadi Harco Leslie Hendric Spits Warnars Kiyota Hashimoto Electrical Engineering Department Universitas Negeri Surabaya Jawa Timur Indonesia Game Application & Technology Program School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Computer Science Esa Unggul University Jakarta Indonesia Information Systems Department Bina Nusantara University Jakarta Indonesia Informatics Department Universitas Negeri Surabaya Jawa Timur Indonesia Information System Department Universitas Budi Luhur Jakarta Indonesia Computer Science department Raharja University Tangerang Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Technology and Environment Prince Songkla University Phuket Campus Thailand

ISBN: (数字)9781728126654

ISBN: (纸本)9781728126661

Currently, the growth of information and technology is rapid. It makes a lot of things in various fields including education becoming more effective and efficient. In education, one of its implementation which is a game is proven to be a useful tool to support conventional teaching methods and bring a more natural understanding of learning materials. Moreover, as a product of the popular culture in modern society, video game mirrors the general culture practice in real life and reflects it via its own culture inside the game. This makes a video game can give a contribution to the social construction of reality as it affects the player's view towards learning in real life and vice versa. From there, we see that there is an opportunity for learning about religion to be supported by its utilization to provide an interactive and fun learning experience. In this paper, we discuss how a video game is implemented to support religious learning. The game was developed with the scrum method where we surveyed to gather the user requirements before the design step. The game design was made by using the use case diagram and storyboard, and it was built using Unity version 2017.3.0f3.

关键词： Games Land mobile radio Education computer science Operating systems Information systems Media

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Correction: The genome of the soybean cyst nematode (Heterodera glycines) reveals complex patterns of duplications involved in the evolution of parasitism genes

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BMC genomics 2024年第1期25卷 537页

作者： Rick Masonbrink Tom R Maier Usha Muppirala Arun S Seetharam Etienne Lord Parijat S Juvale Jeremy Schmutz Nathan T Johnson Dmitry Korkin Melissa G Mitchum Benjamin Mimee Sebastian Eves-van den Akker Matthew Hudson Andrew J Severin Thomas J Baum Department of Plant Pathology Iowa State University Ames IA USA. Genome Informatics Facility Iowa State University Ames IA USA. Agriculture and Agri-Food Canada Saint-Jean-sur-Richelieu QC Canada. Department of Energy Joint Genome Institute Walnut Creek CA USA. HudsonAlpha Institute for Biotechnology Huntsville AL USA. Bioinformatics and Computational Biology Program Worcester Polytechnic Institute Worcester MA USA. Department of Computer Science Worcester Polytechnic Institute Worcester MA USA. Division of Plant Sciences University of Missouri Columbia MO USA. Department of Plant Sciences University of Cambridge Cambridge UK. Department of Crop Sciences University of Illinois Urbana IL USA. Genome Informatics Facility Iowa State University Ames IA USA. severin@iastate.edu.

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A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative

arXiv

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arXiv 2022年

作者： Casiraghi, Elena Wong, Rachel Hall, Margaret Coleman, Ben Notaro, Marco Evans, Michael D. Tronieri, Jena S. Blau, Hannah Laraway, Bryan Callahan, Tiffany J. Chan, Lauren E. Bramante, Carolyn T. Buse, John B. Moffitt, Richard A. Stürmer, Til Johnson, Steven G. Shao, Yu Raymond Reese, Justin Robinson, Peter N. Paccanaro, Alberto Valentini, Giorgio Huling, Jared D. Wilkins, Kenneth J. Bennet, Tell Chute, Christopher DeWitt, Peter Gersing, Kenneth Girvin, Andrew Haendel, Melissa Harper, Jeremy Hajagos, Janos Hong, Stephanie Pfaff, Emily Reusch, Jane Antoniescu, Corneliu Robaski, Kimberly AnacletoLab Department of Computer Science "Giovanni degli Antoni" Università degli Studi di Milano Milan Italy CINI Infolife National Laboratory Roma Italy Department of Biomedical Informatics Stony Brook University Stony BrookNY United States The Jackson Laboratory for Genomic Medicine Farmington United States Institute for Systems Genomics University of Connecticut FarmingtonCT United States Biostatistical Design and Analysis Center Clinical and Translational Science Institute University of Minnesota MinneapolisMN United States Department of Psychiatry Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States University of Colorado Anschutz Medical Campus AuroraCO United States College of Public Health and Human Sciences Oregon State University Corvallis United States Division of General Internal Medicine University of Minnesota MinneapolisMN United States NC Translational and Clinical Sciences Institute University of North Carolina at Chapel Hill Chapel HillNC United States Division of Endocrinology Department of Medicine University of North Carolina School of Medicine United States Department of Epidemiology Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel HillNC United States Institute for Health Informatics University of Minnesota MinneapolisMN United States 260 Longwood Ave Boston United States Department of Radiation Oncology UT Southwestern Medical Center Dallas United States Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory BerkeleyCA United States Fundação Getúlio Vargas Rio de Janeiro Brazil Department of Computer Science Royal Holloway University of London Egham United Kingdom Division of Biostatistics School of Public Health University of Minnesota MinneapolisMN United States Biostatistics Program Office of the Director National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of H

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful to assess associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases and the simple removal of these cases may introduce severe bias. For these reasons, several multiple imputation algorithms have been proposed to attempt to recover the missing information. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithms works best in a given scenario. Furthermore, the selection of each algorithm parameters and data-related modelling choices are also both crucial and challenging. In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. The experiments presented here show that our approach could effectively highlight the most valid and performant missing-data handling strategy for our case study. Moreover, our methodology allowed us to gain an understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types. © 2022, CC BY-NC-ND.

关键词： Data handling

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Genetic Algorithm in Image Inserting with Modified Least Significant Bit Method to Find The Best MSE Value

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Journal of Physics: Conference Series 2020年第1期1566卷

作者： S F Ramadhan P Sihombing Sutarman Department of Information and Technology Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia Master Program (S2) of Informatics Engineering Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia.

Inserting data into digital media tends to change the large size of the media cover file, this is because the inserted pixel has a different size from the insertion value, i.e. with a larger or smaller value, for example a pixel value of 10 is inserted with larger or smaller data from the value 10. So that the pixel value is getting further from the value of 10, this causes the value of Mean Squared Error (MSE) of the media of the cover (cover) to become larger. In this study, the selection of pixels in the image is in accordance with the value of the insertion data as the insertion place with the Genetic algorithm (GA). Insertion of data on selected pixels using the GA algorithm is performed using the Modified Least Significant Bit (MLSB) algorithm at the position of the least significant bits. To find out the reliability of the proposed algorithm, the data insertion experiment was carried out by comparing the conventional LSB algorithm with the proposed algorithm where the results obtained a significant MSE value, i.e. the average MSE value of the LSB algorithm was 20.54 and the proposed algorithm was 7.12.

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The cell surface marker CD36 selectively identifies matured,mitochondria-rich hPSC-cardiomyocytes

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细胞研究（英文版） 2020年第7期30卷 626-629页

作者： Ellen Ngar-Yun Poon Xiao-ling Luo Sarah E.Webb Bin Yan Rui Zhao Stanley Chun Ming Wu Yong Yang Peng Zhang Huajun Bai Jiaofang Shao Ching Man Chan Godfrey Chi-Fung Chan Suk Ying Tsang Rebekah L.Gundry Huang-Tian Yang Kenneth R.Boheler Department of Medicine and Therapeutics and Centre for Cardiovascular Genomics and Medicine The Chinese University of Hong Kong (CUHK) HKSAR China CAS Key Laboratory of Tissue Microenvironment and Tumor Laboratory of Molecular CardiologyShanghai Institute of Nutrition and Health University of Chinese Academy of Sciences (CAS) CAS Shanghai China Division of Life Science and State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology HKSAR China Department of Computer Science The University of Hong Kong (HKU) HKSAR China Intervention and Cell Therapy Center Peking University Shenzhen Hospital Shenzhen China School of Life Sciences CUHK HKSAR China Department of Paediatrics and Adolescent Medicine HKU HKSAR China School of Biomedical Sciences HKU HKSAR China SUSTech Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology (SUSTech) Shenzhen China Department of Bioinformatics School of Biomedical Engineering and InformaticsNanjing Medical University Nanjing Jiangsu China State Key Laboratory of Agrobiotechnology CUHK HKSAR China Department of Biochemistry and Center for Biomedical Mass Spectrometry Research Medical College of Wisconsin Milwaukee WIUSA CardiOmics Program Center for Heart and Vascular Research Division of Cardiovascular Medicine and Department of Cellular and Integrative Physiology University of Nebraska Medical Center Omaha NE USA Institute for Stem Cell and Regeneration Medicine CASBeijing China Division of Cardiology Department of Medicineand Department of Biomedical Engineering The Johns Hopkins University Baltimore MD USA

Dear Editor,Human pluripotent stem cell (hPSC)-derived cardiomyocytes (CMs) are of significant translational value to in vitro studies of human cardiac development, drug and cardiotoxicity testing and cardiac disease *** of hPSCs to CMs,however, yields mixed cultures of atrial-, ventricular-, and pacemaker-like cells as well as non-CMs in variable proportions.1

关键词： Stem-cell differentiation Stem cells

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