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Feasibility study of Information Technology Investment (a case study of ODOO ERP: Project Management Module Implementation in Indonesia Based Company)

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Feasibility study of Information Technology Investment (a ca...

International Conference on ICT For Smart Society (ICISS)

作者： Fredy Jingga Raymond Kosala Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program - Doctor of Computer Science Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

IT Governance are one of the needs in managing Enterprise Level IT. This study shows part of the decision domain of IT Governance Help, which are IT Investment and Prioritization. The purpose of this study is to determine feasibility of implementation open source ERP system Odoo project management module that replaces current system based on operational, economic, and technical aspects. The method used are Fit / Gap Analysis and Cost Benefit model using economic impact worksheet. The analysis is done by comparing the requirement with features owned by current system and Odoo ERP system. This research resulted that Odoo ERP System is feasible in the 3 aspects, the operational feasibility Odoo reaching 72% Fit, the economic feasibility with ROI of 84%, and technical feasibility indicating the technical requirement can be fulfilled by leasing AWS server. The conclusion is the open source ERP system Odoo project management module is feasible to be applied in terms of 3 aspects that have been studied, those are operational, economic, and technical.

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Deep learning-based super-resolution and image transformation into structured illumination microscopy

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Deep learning-based super-resolution and image transformatio...

Microscopy Histopathology and Analytics, Microscopy 2020

作者： Wang, Hongda Rivenson, Yair Jin, Yiyin Wei, Zhensong Gao, Ronald Günaydin, Harun Bentolila, Laurent A. Kural, Comert Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States California NanoSystems Institute University of California Los AngelesCA90095 United States Computer Science Department University of California Los AngelesCA90095 United States Department of Chemistry and Biochemistry University of California Los AngelesCA90095 United States Department of Physics Ohio State University ColumbusOH43210 United States Biophysics Graduate Program Ohio State University ColumbusOH43210 United States Department of Surgery David Geffen School of Medicine University of California Los AngelesCA90095 United States

We demonstrate single-image super-resolution in fluorescence microscopy using a trained neural-network, matching the resolution of structured illumination microscopy (SIM). This performance is achieved without any par... 详细信息

ISBN: (纸本)9781943580743

关键词： Fluorescence microscopy

The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

Sub-picosecond all-optical switching in a hybrid VO2:silicon waveguide at 1550 nm

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arXiv 2020年

作者： Hallman, Kent A. Miller, Kevin J. Baydin, Andrey Weiss, Sharon M. Haglund, Richard F. Department of Physics and Astronomy Vanderbilt University NashvilleTN37235 United States Interdisciplinary Graduate Program in Materials Science NashvilleTN37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37235 United States AEgis Technologies Group 410 Jan Davis Drive HuntsvilleAL35803 United States Department of Electrical and Computer Engineering Rice University HoustonTX70005 United States

Achieving ultrafast all-optical switching in a silicon waveguide geometry is a key milestone on the way to an integrated platform capable of handling the increasing demands for higher speed and higher capacity for information transfer. Given the weak electro-optic and thermo-optic effects in silicon, there has been intense interest in hybrid structures in which that switching could be accomplished by integrating another material into the waveguide, including the phase-changing material, vanadium dioxide (VO2). It has long been known that the phase transition in VO2 can be triggered by ultrafast laser pulses, and that pump-laser fluence is a critical parameter governing the recovery time of thin films irradiated by femtosecond laser pulses near 800 nm. However, thin-film experiments are not a priori reliable guides to using VO2 for all-optical switching in on-chip silicon photonics because of the large changes in VO2 optical constants in the telecommunications band, the requirement of low insertion loss, and the limits on switching energy permissible in integrated photonic systems. Here we report the first measurements to show that the reversible, ultrafast photo-induced phase transition in VO2 can be harnessed to achieve sub-picosecond switching when small VO2 volumes are integrated in a silicon waveguide as a modulating element. Switching energies above threshold are of order 600 fJ/switch. These results suggest that VO2 can now be pursued as a strong candidate for all-optical switching with sub-picosecond on-off times. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical switches

Training of Physical Neural Networks

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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

Code-mixed sentiment analysis of Indonesian language and Javanese language using Lexicon based approach

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Journal of Physics: Conference Series 2021年第1期1869卷

作者： C Tho Y Heryadi L Lukas A Wibowo Department of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia

Nowadays mixing one language with another language either in spoken or written communication has become a common practice for bilingual speakers in daily conversation as well as in social media. Lexicon based approach is one of the approaches in extracting the sentiment analysis. This study is aimed to compare two lexicon models which are SentiNetWord and VADER in extracting the polarity of the code-mixed sentences in Indonesian language and Javanese language. 3,963 tweets were gathered from two accounts that provide code-mixed tweets. Pre-processing such as removing duplicates, translating to English, filter special characters, transform lower case and filter stop words were conducted on the tweets. Positive and negative word score from lexicon model was then calculated using simple mathematic formula in order to classify the polarity. By comparing with the manual labelling, the result showed that SentiNetWord perform better than VADER in negative sentiments. However, both of the lexicon model did not perform well in neutral and positive sentiments. On overall performance, VADER showed better performance than SentiNetWord. This study showed that the reason for the misclassified was that most of Indonesian language and Javanese language consist of words that were considered as positive in both Lexicon model.

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Bright high-purity quantum emitters in aluminium nitride integrated photonics

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arXiv 2020年

作者： Lu, Tsung-Ju Lienhard, Benjamin Jeong, Kwang-Yong Moon, Hyowon Iranmanesh, Ava Grosso, Gabriele Englund, Dirk Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Photonics Initiative Advanced Science Research Center and Physics Program Graduate Center City University of New York New YorkNY10031 United States Department of Physics Korea University Seoul02841 Korea Republic of

Solid-state quantum emitters (QEs) are fundamental in photonic-based quantum information processing. There is strong interest to develop high-quality QEs in III-nitride semiconductors because of their sophisticated manufacturing driven by large and growing applications in optoelectronics, high voltage power transistors, and microwave amplifiers. Here, we report the generation and direct integration of QEs in an aluminium nitride-based photonic integrated circuit platform. For individual waveguide-integrated QEs, we measure an off-chip count rate exceeding 6 × 104 counts per second (cps) (saturation rate > 8.6 × 104 cps). In an unpatterned thin-film sample, we measure antibunching with g(2)(0) ~ 0.05 and photon count rates exceeding 8 × 105 cps (saturation rate > 1 × 106 cps). Although spin and detailed optical linewidth measurements are left for future work, these results already show the potential for high-quality QEs monolithically integrated in a wide range of III-nitride device technologies that would enable new quantum device opportunities and industrial scalability. Copyright © 2020, The Authors. All rights reserved.

关键词： III-V semiconductors

Author Correction: Generation of accurate, expandable phylogenomic trees with uDance

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Nature biotechnology 2024年第5期42卷 814页

作者： Metin Balaban Yueyu Jiang Qiyun Zhu Daniel McDonald Rob Knight Siavash Mirarab Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA. School of Life Sciences Arizona State University Tempe AZ USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering Jacobs School of Engineering University of California San Diego La Jolla CA USA. Department of Bioengineering University of California San Diego La Jolla CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. smirarab@ucsd.edu. Department of Computer Science and Engineering Jacobs School of Engineering University of California San Diego La Jolla CA USA. smirarab@ucsd.edu. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. smirarab@ucsd.edu.

Design approach for additive manufacturing in spare part supply chains

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arXiv 2020年

作者： de Brito, Filipe Marinho da Cruz Júnior, Gélson Frazzon, Enzo Morosini Basto, João Pedro Tavares Vieira Alcalá, Symone Gomes Soares School of Electrical Mechanical and Computer Engineering Federal University of Goiás GoiâniaGoiás74605-010 Brazil INESC P&D Brasil São Paulo11055-300 Brazil Graduate Program in Production Engineering Department of Production Engineering and Systems Federal University of Santa Catarina Florianópolis88040-900 Brazil Department of Industrial Engineering and Management Faculty of Engineering University of Porto Porto4200-465 Portugal INESC TEC - Technology Faculty of Engineering University of Porto Porto4200-465 Portugal Production Engineering Faculty of Sciences and Technology Federal University of Goiás Aparecida de Goiânia Goiás74968-755 Brazil

In the current industrial revolution, additive manufacturing (AM) embodies a promising technology that can enhance the effectiveness, adaptability, and competitiveness of supply chains (SCs). Moreover, it facilitates the development of distributed SCs, thereby enhancing product availability, inventory levels, and lead time. However, the wide adoption of AM in industrial SCs creates various challenges, leading to new difficulties for SC design. In this context, this paper proposes a new design approach to AM SCs using optimization methods. More specifically, the proposed approach, comprising the pmedian and mixed-integer linear programming models, considers the decision of deploying productive resources (3D printers) in specific locations of generic spare part SCs. The approach was evaluated in a real-world use case of an elevator maintenance service provider. The obtained results demonstrated the promising capabilities of the proposed design approach in managing the challenges arising from the forthcoming widespread use of 3D printers in manufacturing SCs. © 2020, CC-BY-NC-ND.

关键词： 3D printers