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An Improved Version of Texture-based Foreground Segmentation: Block-based Adaptive Segmenter

Procedia computer science 2018年 135卷 579-586页

作者： Kahlil Muchtar Faris Rahman Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Nodeflux Inc. Jakarta Indonesia Syiah Kuala University Aceh Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Foreground segmentation is one of moving object detection techniques of computer vision applications. To date, modern moving object detection methods require complex background modeling and thresholds tuning to confront illumination changes. This paper proposes an adaptive approach based on non-overlapping block texture representation. It aims to design a computationally light and efficient solution to improve the robustness of detection. We evaluate our proposed method on internal and public sequences and provide the quantitative and qualitative measurements. Experimental results show that the proposed method can improve the results of previous method and suitable for real-time challenges.

关键词： change detection foreground segmentation texture features

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Development Conceptual Model and Validation Instrument for E-Learning Succes Model at Universities in Indonesia: Perspectives influence of Instructor's Activities and Motivation

Development Conceptual Model and Validation Instrument for E...

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International Congress on Applied Information Technology

作者： Titan Erick Fernando Dina Fitria Murad Harco Leslie Hendric Spits Warnars Kristianus Oktriono BINUS Online Learning Bina Nusantara University Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Language Center Faculty of Humanities Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728158624

ISBN: (纸本)9781728158631

The purpose of this study is to develop conceptual models and validation instruments for the success of e-learning systems in Indonesian universities from factors that influence instructor activity and user motivation. Model development is based on the IS success model of DeLone and Mclean by adding instructor activity and motivational variables based on related literature studies. Evaluation is done by testing the reliability and validity of the instrument. Data collection by distributing instruments online with the snowball method, respondents obtained were 234 used as data analysis with SmartPLS 2.0 software. The results of this study produced a conceptual model consisting of 9 variables and 83 instruments which were validated according to the provisions. From these results, it can be implemented in future research by increasing larger respondents to get results that represent the success of e-learning systems at universities in Indonesia.

关键词： Electronic learning Information systems Instruments Reliability Learning systems Organizations Computational modeling

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Towards decoding the coupled decision-making of metabolism and epithelial-mesenchymal transition in cancer

arXiv

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arXiv 2020年

作者： Jia, Dongya Park, Jun Hyoung Kaur, Harsimran Jung, Kwang Hwa Yang, Sukjin Tripathi, Shubham Galbraith, Madeline Deng, Youyuan Jolly, Mohit Kumar Kaipparettu, Benny Abraham Onuchic, José N. Levine, Herbert Center for Theoretical Biological Physics Rice University HoustonTX77005 United States Department of Molecular and Human Genetics Baylor College of Medicine HoustonTX77030 United States Centre for BioSystems Science and Engineering Indian Institute of Science BangaloreKarnataka560012 India PhD Program in Systems Synthetic and Physical Biology Rice University HoustonTX77005 United States Department of Physics Northeastern University BostonMA02115 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Rice University HoustonTX77005 United States Dan L. Duncan Comprehensive Cancer Center Baylor College of Medicine HoustonTX77030 United States Department of Chemistry Rice University HoustonTX77005 United States Department of Biosciences Rice University HoustonTX77005 United States Department of Bioengineering Northeastern University BostonMA02115 United States

Cancer cells have the plasticity to adjust their metabolic phenotypes for survival and metastasis. During metastasis, a developmental program known as the epithelial-mesenchymal transition (EMT) plays a critical role. There is extensive cross-talk between metabolism and EMT, but how this leads to coordinated physiological changes is still uncertain. The elusive connection between metabolism and EMT compromises the efficacy of metabolic therapies targeting metastasis. In this review, we aim for clarifying causation between metabolism and EMT based on recent experimental studies and propose integrated theoretical-experimental efforts to better understand the coupled decision-making of metabolism and EMT. © 2020, CC-BY-NC-ND.

关键词： Metabolism

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Reconfigurable Hyperbolic Polaritonics with Correlated Oxide Metasurfaces

Research Square

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Research Square 2021年

作者： Aghamiri, Neda Alsadat Hu, Guangwei Fali, Alireza Zhang, Zhen Li, Jiahan Balendhran, Sivacarendran Walia, Sumeet Sriram, Sharath Edgar, James H. Ramanathan, Shriram Alù, Andrea Abate, Yohannes Department of Physics and Astronomy University of Georgia AthensGA30602 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Department of Electrical and Computer Engineering National University of Singapore Kent Ridge Singapore117583 Singapore School of Materials Engineering Purdue University West LafayetteIN47907 United States Tim Taylor Department of Chemical Engineering Kansas State University ManhattanKN 66506 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia School of Engineering RMIT University MelbourneVIC Australia Functional Materials and Microsystems Research Group Micro Nano Research Facility RMIT University MelbourneVIC Australia ARC Centre of Excellence for Transformative Meta-Optical Systems RMIT University MelbourneVIC Australia Physics Program Graduate Center City University of New York New YorkNY10016 United States

Polaritons enable subwavelength confinement and highly anisotropic flows of light over a wide spectral range, holding the promise for applications in modern nanophotonic and optoelectronic devices. However, to fully realize their practical application potential, facile methods enabling nanoscale active control of polaritons are needed. Here, we introduce a hybrid polaritonic-oxide heterostructure platform consisting of van der Waals crystals, such as hexagonal boron nitride (hBN) or alpha-phase molybdenum trioxide (α-MoO3), transferred on nanoscale oxygen vacancy patterns on the surface of prototypical correlated perovskite oxide SmNiO3 (SNO). Using a combination of scanning probe microscopy and infrared nanoimaging techniques, we demonstrate nanoscale real-time reconfigurability of complex hyperbolic phonon polaritons patterned at the nanoscale with unmatched resolution. Hydrogenation and temperature modulation allow spatially localized conductivity modulation of SNO nanoscale patterns, enabling robust real-time modulation and nanoscale reconfiguration of hyperbolic polaritons. Our work paves the way towards nanoscale programmable metasurface engineering as a new paradigm for reconfigurable nanophotonic applications facilitated by a hybrid material platform exploiting extreme light-matter interactions in polaritonic systems. © 2021, CC BY.

关键词： Polariton

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Caffeic acid phenethyl ester suppresses EGFR/FAK/Akt signaling, migration, and tumor growth of prostate cancer cells

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Phytomedicine 2023年 116卷

作者： Jen-Chih Tseng Bi-Juan Wang Ya-Pei Wang Ying-Yu Kuo Jen-Kun Chen Tzyh-Chyuan Hour Li-Kuo Kuo Po-Jen Hsiao Chien-Chih Yeh Cheng-Li Kao Li-Jane Shih Chih-Pin Chuu Institute of Cellular and System Medicine National Health Research Institutes Miaoli County 35053 Taiwan Immunology Research Center National Health Research Institutes Miaoli County 35053 Taiwan Institute of Biomedical Engineering and Nanomedicine National Health Research Institutes Miaoli County 35053 Taiwan Neuroscience Research Center Kaohsiung Medical University Kaohsiung 80737 Taiwan Department of Biochemistry School of Medicine Kaohsiung Medical University Kaohsiung 80737 Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung 80737 Taiwan Division of Pulmonary and Critical Care Medicine Department of Internal Medicine Mackay Memorial Hospital Taipei City 104217 Taiwan Department of Nursing Mackay Medical College Taipei City Taiwan Division of Nephrology Department of Internal Medicine Taoyuan Armed Forces General Hospital Taoyuan 325 Taiwan Division of Nephrology Department of Internal Medicine Tri-Service General Hospital National Defense Medical Center Taipei 114 Taiwan Department of Education and Medical Research Taoyuan Armed Forces General Hospital Taoyuan Taiwan Division of Colon and Rectal Surgery Department of Surgery Tri-Service General Hospital National Defense Medical Center Taipei Taiwan Division of Urology Departments of Surgery Tri-Service General Hospital National Defense Medical Center Taipei Taiwan Division of Urology Department of Surgery Taoyuan Armed Forces General Hospital Taoyuan Taiwan Graduate Institute of Medical Science National Defense Medical Center Taipei 114 Taiwan Department of Life Sciences National Central University Taoyuan 32031 Taiwan PhD Program for Aging and Graduate Institute of Basic Medical Science China Medical University Taichung City 40402 Taiwan Biotechnology Center National Chung Hsing University Taichung City 40227 Taiwan

Background Epidermal growth factor receptor (EGFR) is upregulated in prostate cancer (PCa). However, suppression of EGFR did not improve the patient outcome, possibly due to the activation of PI3K/Akt signaling in PCa. Compounds able to suppress both PI3K/Akt and EGFR signaling may be effective for treating advanced PCa. Purpose We examined if caffeic acid phenethyl ester (CAPE) simultaneously suppresses the EGFR and Akt signaling, migration and tumor growth in PCa cells. Methods Wound healing assay, transwell migration assay and xenograft mice model were used to determine the effects of CAPE on migration and proliferation of PCa cells. Western blot, immunoprecipitation, and immunohistochemistry staining were performed to determine the effects of CAPE on EGFR and Akt signaling. Results CAPE treatment decreased the gene expression of HRAS, RAF1, AKT2, GSK3A , and EGF and the protein expression of phospho-EGFR (Y845, Y1069, Y1148, Y1173), phospho-FAK, Akt, and ERK1/2 in PCa cells. CAPE treatment inhibited the EGF-induced migration of PCa cells. Combined treatment of CAPE with EGFR inhibitor gefitinib showed additive inhibition on migration and proliferation of PCa cells. Injection of CAPE (15 mg/kg/3 days) for 14 days suppressed the tumor growth of prostate xenografts in nude mice as well as suppressed the levels of Ki67, phospho-EGFR Y845, MMP-9, phospho-Akt S473, phospho-Akt T308, Ras, and Raf-1 in prostate xenografts. Conclusions Our study suggested that CAPE can simultaneously suppress the EGFR and Akt signaling in PCa cells and is a potential therapeutic agent for advanced PCa.

关键词： Prostate cancer Caffeic acid phenethyl ester Metastasis Epidermal growth factor receptor Fak Akt CAPE caffeic acid phenethyl ester CRPC castration-resistant prostate cancer EGFR epidermal growth factor receptor EMT epithelial-mesenchymal transition GAPDH glyceraldehyde-3-phosphate dehydrogenase mTOR mammalian target of rapamycin PCa prostate cancer PI3K phosphoinositide 3-kinase PSA prostate specific antigen PSMA prostate specific-membrane antigen PTEN phosphatase and tensin homolog

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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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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Corrigendum to “Metal ratio and bimetal nanoarchitectonics of ammonia-based fluoride complex induced nickel hydroxide and manganese oxide composites as active materials of an energy storage device” [J. Energy Storage 80 (2024) 110316]

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Journal of Energy Storage 2025年 108卷

作者： Chuan-Chih Hsu You Zher Yu Chung-Hsien Wu Pin-Yan Lee Hung-Ming Chen Sadang Husain Chutima Kongvarhodom Yu-Cheng Hsiao Lu-Yin Lin Division of Cardiovascular Surgery Department of Surgery School of Medicine College of Medicine Taipei Medical University Taiwan Division of Cardiovascular Surgery Department of Surgery Taipei Medical University Hospital Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan Gingen Technology Co. LTD. Rm. 7 10F. No.189 Sec. 2 Keelung Rd. Xinyi Dist. Taipei 11054 Taiwan Department of Physics Faculty of Mathematics and Natural Science Lambung Mangkurat University Banjarmasin 70124 Indonesia Department of Chemical Engineering King Mongkut's University of Technology Thonburi 126 Pracha-u-thit Toong-kru Bangkok 10140 Thailand Cell Physiology and Molecular Image Research Center Wan Fang Hospital Taipei Medical University Taipei 11696 Taiwan International PhD Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan Stanford Byers Center for Biodesign Stanford University CA 94305-5428 USA Graduate Institute of Biomedical Optomechatronics College of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan

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Unusable Spoken Response Detection with BLSTM Neural Networks

Unusable Spoken Response Detection with BLSTM Neural Network...

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International Symposium on Chinese Spoken Language Processing

作者： Zhaoheng Ni Rutuja Ubale Yao Qian Michael Mandel Su-Youn Yoon Abhinav Misra David Suendermann-Oeft Computer Science Program The Graduate Center City University of New York Educational Testing Service Research USA Computer and Information Science Brooklyn College City University of New York

ISBN: (纸本)9781538656280;9781538656273

Voice biometrics has been applied to enhance the security of spoken language proficiency tests and ensure valid test scores by detecting fraudulent activity. These methods can, however, be triggered by certain distortions, including background noise and adjacent test-takers, resulting in false positive alarms. In this paper, a two-layer bi-directional LSTM RNN model is employed to detect these distorted (unusable) responses and a sub-sampling method is applied to reduce the difficulties of model training caused by very long input sequence and imbalanced training data. The system is evaluated on a corpus that was collected from an assessment of English language proficiency around the world. Results show that our approach significantly outperforms two baselines: a Gaussian mixture model (GMM) classifying frame-level features and an AdaBoost classifier operating on i-vectors. Our system's F-score in unusable response detection is 0.60 compared to 0.43 and 0.49 for the two baseline systems.

关键词： Feature extraction Task analysis Training Speech recognition Neural networks Data models Training data

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Analyzing human and machine performance in resolving ambiguous spoken sentences 1

Analyzing human and machine performance in resolving ambiguo...

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1st Workshop on Speech-Centric Natural Language Processing, SCNLP 2017, held in conjunction with the Conference on

作者： Ghaly, Hussein Mandel, Michael I. City University of New York Graduate Center Linguistics Program United States City University of New York Graduate Center Computer Science Program United States

ISBN: (纸本)9781945626920

Written sentences can be more ambiguous than spoken sentences. We investigate this difference for two different types of ambiguity: Prepositional phrase (PP) attachment and sentences where the addition of commas changes the meaning. We recorded a native English speaker saying several of each type of sentence both with and without disambiguating contextual information. These sentences were then presented either as text or audio and either with or without context to subjects who were asked to select the proper interpretation of the sentence. Results suggest that commaambiguous sentences are easier to disambiguate than PP-attachmentambiguous sentences, possibly due to the presence of clear prosodic boundaries, namely silent pauses. Subject performance for sentences with PP-attachment ambiguity without context was 52% for text only while it was 72.4% for audio only, suggesting that audio has more disambiguating information than text. Using an analysis of acoustic features of two PP-attachment sentences, a simple classifier was implemented to resolve the PP-attachment ambiguity being early or late closure with a mean accuracy of 80%. © 2017 EMNLP 2017 - 1st Workshop on Speech-Centric Natural Language Processing, SCNLP 2017 - Proceedings of the Workshop. All rights reserved.

关键词： Natural language processing systems

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