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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Self-Awareness for Autonomous systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Critical mass effect in evolutionary games triggered by zealots

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Physical Review Research 2020年第2期2卷 023305-023305页

作者： Alessio Cardillo Naoki Masuda Department of Engineering Mathematics University of Bristol Bristol BS8 1UB United Kingdom Department of Computer Science and Mathematics University Rovira i Virgili E-43007 Tarragona Spain GOTHAM Lab – Institute for Biocomputation and Physics of Complex Systems (BIFI) University of Zaragoza E-50018 Zaragoza Spain Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Tiny perturbations may trigger large responses in systems near criticality, shifting them across equilibria. Committed minorities are suggested to be responsible for the emergence of collective behaviors in many physical, social, and biological systems. Using evolutionary game theory, we address the question whether a finite fraction of zealots can drive the system to large-scale coordination. We find that a tipping point exists in coordination games, whereas the same phenomenon depends on the selection pressure, update rule, and network structure in other types of games. Our study paves the way to understand social systems driven by the individuals' benefit in the presence of zealots, such as human vaccination behavior or cooperative transports in animal groups.

关键词： Patterns in complex systems Social systems

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ELM vs. WiSARD: A performance comparison 25

ELM vs. WiSARD: A performance comparison

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25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017

作者： Oliveira, Luiz Fernando R. França, Felipe M.G. Systems Engineering and Computer Science Program COPPE Universidade Federal Do Rio de Janeiro Brazil

ISBN: (纸本)9782875870391

The extreme learning machine (ELM) is known for being a fast learning neural model. This work presents a performance comparison between ELM and the WiSARD weightless neural network model, regarding training and testing times, and classification accuracy as well. The two models were implemented in the same programming language and experiments were carried out on the same hardware environment. By using a group of datasets from the public repositories UCI and Statlog, experimental results shows that the WiSARD presented training times approximately one order of magnitude smaller than ELM, while classification accuracy varied according the number of classes involved. However, while WiSARD's architecture setups were not exhaustively searched, architecture setups for ELM were kept the same as the ones found in the literature as the best for each given dataset. © ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.

关键词： Classification (of information)

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Software Process Definition using Process Lines: A Systematic Literature Review

Software Process Definition using Process Lines: A Systemati...

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Conferencia Latinoamericana En Informatica (CLEI)

作者： Diogo Matheus Costa Eldânae Nogueira Teixeira Claudia Maria Lima Werner Systems Engineering and Computer Science Department PESC/COPPE/UFRJ Rio de Janeiro Brazil

ISBN: (纸本)9781728104386

Software processes have been the focus of discussion in literature, but defining a software process remains a challenge. The Software Process Line (SPrL) technique offers a systematic process reuse approach that identifies processes' similarities and variability. This study aims to characterize the state-of-the-art of the software process definition using SPrL. A Systematic Literature Review (SLR) was conducted and 26 papers were identified to be studied in depth. The results indicate a concentration of approaches that use mapping/rules techniques to support variability resolution and the need for integrated supporting tools. In addition, three problems that affect the software process definition using SPrL were observed: (1) low understanding of SPrL models complexity and their impact, (2) lack of understanding about the impacts of the defined software process, and (3) experts' knowledge dependence issues in decision-making to solve variability.

关键词： Software Bibliographies Tools Systematics Industries Process control systems engineering and theory

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A Novel Ensemble XGBoost and Deep Q-Network for Pregnancy Risk Prediction on Multi-Class Imbalanced Datasets

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ICT Express 2025年

作者： Kurnianingsih Sou Nobukawa Melyana Nurul Widyawati Cipta Pramana Nurseno Bayu Aji Afandi Nur Aziz Thohari Dwiana Hendrawati Eri Sato-Shimokawara Naoyuki Kubota Department of Electrical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Semarang 50275 Central Java Indonesia Department of Computer Science Chiba Institute of Technology 2-17-1 Tsudanuma Narashino 275-0016 Chiba Japan Department of Postgraduate Program Poltekkes Kemenkes Semarang Jl. Tirto Agung Pedalangan Kecamatan Banyumanik Semarang 50268 Central Java Indonesia Department of Obstetrics and Gynecology KRMT Wongsonegoro Hospital Jl. Tembalang Semarang 50272 Central Java Indonesia Department of Mechanical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Semarang 50275 Central Java Indonesia Faculty of Systems Design Tokyo Metropolitan University Tokyo 1910065 Japan

Addressing imbalanced data is essential for accurate prediction. We propose a novel ensemble method of XGBoost and deep Q-learning networks (DQN) for pregnancy risk prediction. First, we train the majority class utilizing XGBoost. We subsequently utilize DQN to train the minority class into binary classifications. Finally, we use the trained models from DQN and XGBoost in ensemble learning to generate the final classification results. The XGBoost-DQN model achieves high performance with 0.9819 in precision, recall, F1-score, and accuracy, outperforming several baseline classifiers on private data from 5,313 pregnant women in Indonesia and showing superior results on public datasets.

关键词： Imbalanced dataset ensemble learning XGBoost Tree Deep Q-Network pregnancy risk

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Warding off the plagiarism with the applications (Case study at Bina Nusantara University student and faculty member) 1

Warding off the plagiarism with the applications (Case study...

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

作者： Surjandy Suparta, Wayan Trisetyarso, Agung Kang, Chul Ho Abbas, Bahtiar Saleh Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information Systems Audit Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Civil Engineering Department University of Technology Yogyakarta Yogyakarta Indonesia Department of Electronics and Communication Engineering Kwangwoon University Korea Republic of Department of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538609545

Plagiarism in the education sector is as serious issues. In 2015 Indonesia ministry of research, technology and higher education stated to eradicate the plagiarism, and after 2 years the similar statement by Indonesia ministry of research, technology, and higher education. The plagiarism becomes the serious program by the ministry. The plagiarism in higher education happened, the faculty member or even a professor found did the plagiarism. Therefore, this paper research conducted using descriptive analysis methodology to elaborate on deep the correlation plagiarism variables. Hence, warding off the plagiarism might possible to perform using the applications. The aim of this research to find the correlation variables that might use to ward off the plagiarism in the future. The very interesting result from 15 questioners and the report shows there are several significant correlation variables of the plagiarism. © 2018 IEEE.

关键词： Intellectual property

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Designing an Institutional Research Management System Framework for Higher Education 3

Designing an Institutional Research Management System Framew...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Noerlina Elda Hiererra, Siti Saleh Abbas, Bahtiar Andro Makalew, Brilly Maya Kristin, Desi Nugraha Mursitama, Tirta David Information Systems Department Bina Nusantara University School of Information Systems Jakarta11480 Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Mobile Application Technology Program Jakarta11480 Indonesia Computer Science Department Bina Nusantara University School of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

Although research activity plays a big role in institutional ranking and provides a great benefit for university lecturer, the process of managing all research activity is tedious and exhausting. In most university, management must be able to collect all hundreds of proposals and reports;not to mention they must be able to accommodate the process of reviewing every single proposal. They also need to monitor all research output in final report and make sure that all of the outputs are correctly achieved from proposed target. In addition to the problems stated, previous activities are done manually, leaving a lot of opportunity to improve and automate the process. All of the addressable opportunity for improvement are explained and explored, thus a model of the web application is created. This paper presents the design of web application used to solve persistent problem in research management, specifically in Binus University. The design methodology is divided into two phases: requirement engineering phase, and system design. Upon finishing the requirement engineering phase, system model is depicted using Unified Modeling Language, specifically: Activity Diagram, Use Case Diagram, and Conceptual Class Diagram. © 2018 IEEE.

关键词： Unified Modeling Language

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The Affecting Factors of Blockchain Technology Adoption of Payments systems in Indonesia Banking Industry 3

The Affecting Factors of Blockchain Technology Adoption of P...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Taufiq, Rohmat Meyliana Hidayanto, Achmad Nizar Prabowo, Harjanto Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Informatic Engineering Engineering Faculty University Muhammadiyah of Tangerang Tangerang15117 Indonesia Information Systems Department Bina Nusantara University School of Information Systems Jakarta11480 Indonesia Faculty of Computer Science University of Indonesia Depok16424 Indonesia Management Department Bina Nusantara University BINUS Business School Undergraduate Program Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

The purpose of this study is to find the influence factors of blockchain technology adoption of payments system in Indonesia banking industry. Moreover, the researchers proposed new model of payment system by using blockchain technology in Indonesia banking industry. All this time, the researchers of blockchain in payment systems were done in major countries such as UK, USA, Australia, Scotland, Germany, Japan, France, Italy, and China. Systematic Literature Review (SLR) used as a method in this study and there were five databases that taken as references, such as IEEE, science direct, AIS electronic, ACM and Google Scholar. The researchers were found 26 references Studies Included in Quantitative Synthesis and 9 variables (Easy to use, Usefulness, Transaction fees, Risk perception, Behaviour intention of use, Attitude toward, Cognitive style, Subjective Norm, Effectiveness). Based on the result of data analysis and discussion, there were two conclusions in this study, the first, the researchers found nine factors that expected tobe used as a reference if Indonesia banking industry wants to adopt blockchain technology for payment system;and second, the researchers had to propose payment system model by using blockchain technology in Indonesia banking industry. © 2018 IEEE.

关键词： Blockchain

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Optimized feature selection for initial launch in dynamic software product lines 20

Optimized feature selection for initial launch in dynamic so...

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20th International Conference on Enterprise Information systems, ICEIS 2018

作者： De Sousa Santos, Ismayle Junior, Evilasio Costa De Castro Andrade, Rossana Maria Dos Santos Neto, Pedro De Alcântara Rocha, Leonardo Sampaio Werner, Claudia Maria Lima De Souza, Jerffeson Texeira Department of Computer Science Federal University of Ceará Fortaleza Brazil Department of Computer Science Federal University of Piauí Teresina Brazil Science and Tecnology Center State University of Ceará Fortaleza Brazil Computer Systems Engineering Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9789897582981

A Dynamic Software Product Line (DSPL) allows the generation of products that can adapt dynamically according to changes in requirements or environment at runtime. This runtime adaptation is often made by the activation and deactivation of features, introducing a cost (e.g., an overhead regarding resource consumption). To reduce this cost, a solution is the partial product configuration at the static binding time. Thus, in DSPLs, one challenge is the feature selection to define which features should be bound permanently before the initial launch and which features should be bound at runtime. In this paper, we address this challenge presenting a graph model formulation to the feature selection problem for the initial launch in DSPLs that considers both static and dynamic binding. This model allows the application of efficient optimization algorithms to solve the problem. We also present a proof of concept showing that the model can be used to generate optimized solutions to the feature selection problem for initial launch in DSPLs. © 2018 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Multiobjective optimization

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