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Multi-criteria decision making on succesfull of online learning using AHP and regression

Journal of Physics: Conference Series 2019年第1期1175卷

作者： Fatsyahrina Fitriastuti Uci Rahmalisa Abba Suganda Girsang Department of Informatics Engineering Janabadra University Yogyakarta Indonesia Department of Informatics Engineering STMIK Hang Tuah Pekan Baru Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This paper purposes comparing the view of system builder (expert system) using analitycal hierarchy process (AHP) and view of user (students) using regression method in elearning system STIKes-STMIK Hang Tuah Pekanbaru. This study uses some attributes to analyze and evaluate which affect the acceptance of elearning system success. In AHP, the attributes are categorized as criteria and grouped into some dimensions. These criteria and dimenssions are evaluated by some experts using analitycal hierarchy process (AHP) to assess priorities for elearning system. There are four dimensions, system quality (four criteria), learning information quality (three criteria), service quality (three criteria) and service support (three criteria) dimension. This system is also evaluated by filling out the questionnaire by students as user of elearning system using regression method. The attributes are used as independent variable, while the succesfull of elearning is dependent variable. The result of using AHP and regression system has a different. It also can show there are different view between users and system builder which should be considered by STIKes-STMIK Hang Tuah Pekanbaru.

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Multi-UAVs communication for payload data link enhancement under asynchronous access

Multi-UAVs communication for payload data link enhancement u...

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International Conference on Wireless and Optical Communications ( WOCC)

作者： Yun-Ruei Li Hsin-Piao Lin Li-Chun Wang Abebe Belay Adege Li-Yong Chen Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering National Chiao Tung University Hsinchu Taiwan Department of International Graduate Program in Electrical Engineering and Computer Science (EECS) National Taipei University of Technology Taipei Taiwan

Recently, unmanned Aerial Vehicles (UAVs) are widely applied in civilian and commercial areas. In order to rapidly arrange the UAVs in a variable and changeable environment, the technique of maintaining stable and reliable communication link between Ground Control Station (GCS) and UAVs is one of the main issues [1]. However, UAVs deployment can significantly increase the efficiency of missions execution and the flexibility of task allocation. As discussed by different scholars, multi-UAVs have a number of advantages depending on the application areas: disaster areas monitoring, wireless sensor data collection in precision agriculture, and relay-aided of UAVs network topology [2].

关键词： OFDM Resource management Interference Wireless communication Payloads Bandwidth Optical filters

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Design of an autonomous aerator 22

Design of an autonomous aerator

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22nd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2017

作者： Goelzer, Rilene Carolina Ávila, Letieri Oliveira, Deliel De Oliveira, Vinícius M. Control and Automation Engineering Federal University of Rio Grande Rio Grande RS Brazil Graduate Program in Computer Engineering Federal University of Rio Grande Rio Grande RS Brazil

ISBN: (纸本)9781509065059

Aquaculture has resorted to the use of technology to make control of important variables and improve production. There were two problems observed in the Marine Aquaculture Station - EMA of the Federal University of Rio Grande, more specifically related to the aeration of the culture tanks to pisciculture and carniciculture. Firstly, the high energy consumption of the paddle aerators was identified, due to the continuous need to oxygenate the water. Subsequently, it was noted that static aerators do not aerate the water evenly and they require that their placement be done manually by technicians or assistants. Thus, the article proposes a design of an autonomous aerator with electric power coming from a photovoltaic solar energy system. In the sequence, a navigation system for the aerator is proposed along the tank, based on a differential performance of the motors connected to the paddle. © 2017 IEEE.

关键词： Solar energy

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A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems

A Neuro-Inspired Approach to Solve a Simultaneous Location a...

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International Conference on Control, Automation, Robotics and Vision

作者： Matheus Chaves Menezes Edison Pignaton de Freitas Sen Cheng Alexandre Cesar Muniz de Oliveira Paulo Rogerio de Almeida Ribeiro Graduate Program on Computer Science Federal University of Maranhão São Luís Brazil Graduate Program on Computer Science Federal University of Rio Grande do Sul Porto Alegre Brazil Computational Neuroscience Ruhr University Bochum Bochum Germany Department of Informatics Federal University of Maranhão São Luís Maranhão Department of Computer Engineering Federal University of Maranhão São Luís Maranhão

This paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. This proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. The work here reported extends this approach by merging partial information acquired by multiple robots. The results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. Thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots.

关键词： Navigation Simultaneous localization and mapping Feature extraction Robot vision systems Task analysis Hippocampus

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Generative Adversarial Networks Enable Cross-Modality Super-Resolution in Fluorescence Microscopy

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Microscopy and Microanalysis 2019年第S2期25卷 1228-1229页

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

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Cryptocurrencies for Smart Territories: an exploratory study

Cryptocurrencies for Smart Territories: an exploratory study

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International Joint Conference on Neural Networks (IJCNN)

作者： Alexandre C. Barbosa Thays A. Oliveira Vitor N. Coelho Graduate Program in Sustainable Territorial Development Universitá Degli Studi di Padova Italy and Katholieke Universiteit Leuven Belgium Universitè Paris 1 Pantheon-Sorbonne France Graduate Program in Engineering and Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Institute of Computer Science Universidade Federal Fluminense Niterói RJ Brazil

The smart city debate will still be the scope of business, policy-making and territorial planning in the following years. Based on that, the paper aims to propose an interdisciplinary conceptualization of smart territories. The goal is to promote the contrary movement of megacities creation and also consolidate the geographical aspect of smartness. The concept was formulated based on the elements of sustainable territorial development, strategic spatial planning and smart governance. In addition, taking hand of a parallel literature review, the study explores the potential of creating complementary cryptocurrencies as a strategy of territorial planning. This term was built based on already existing complementary currency systems in distributed ledger technologies scope. The idea provided presents a great potential for poverty alleviation by bringing together financial and digital inclusion.

关键词： Planning Smart cities Collaboration Decision making Government Cryptography Cultural differences

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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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Considering discrepancy when calibrating a mechanistic electrophysiology model

arXiv

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arXiv 2020年

作者： Lei, Chon Lok Ghosh, Sanmitra Whittaker, Dominic G. Aboelkassem, Yasser Beattie, Kylie A. Cantwell, Chris D. Delhaas, Tammo Houston, Charles Novaes, Gustavo Montes Panfilov, Alexander V. Pathmanathan, Pras Riabiz, Marina Weber dos Santos, Rodrigo Worden, Keith Mirams, Gary R. Wilkinson, Richard D. Computational Biology & Health Informatics Dept. of Computer Science University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Centre for Mathematical Medicine & Biology School of Mathematical Sciences University of Nottingham United Kingdom Department of Bioengineering University of California San Diego United States Systems Modeling and Translational Biology GlaxoSmithKline R&D Stevenage United Kingdom ElectroCardioMaths Programme Centre for Cardiac Engineering Imperial College London United Kingdom CARIM School for Cardiovascular Diseases Maastricht University Netherlands Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil Department of Physics and Astronomy Ghent University Belgium Laboratory of Computational Biology and Medicine Ural Federal University Ekaterinburg Russia U.S. Food and Drug Administration Center for Devices and Radiological Health Office of Science and Engineering Laboratories United States Department of Biomedical Engineering King’s College London Alan Turing Institute United Kingdom Dynamics Research Group Department of Mechanical Engineering University of Sheffield United Kingdom School of Mathematics and Statistics University of Sheffield United Kingdom

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Superiorization of preconditioned conjugate gradient algorithms for tomographic image reconstruction

arXiv

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arXiv 2018年

作者： Helou, Elias S. Herman, Gabor T. Lin, Chuan Zibetti, Marcelo V.W. Institute of Mathematical Science and Computation University of São Paulo SP Brazil Department of Computer Science Graduate Center City University of New York NY United States School of Electrical Engineering Southwest Jiaotong University Chengdu China Program in Electrical and Computer Engineering Federal University of Technology PR Brazil

Properties of Superiorized Preconditioned Conjugate Gradient (SupPCG) algorithms in image reconstruction from projections are examined. Least squares (LS) is usually chosen for measuring data-inconsistency in these inverse problems. Preconditioned Conjugate Gradient algorithms are fast methods for finding an LS solution. However, for ill-posed problems, such as image reconstruction, an LS solution may not provide good image quality. This can be taken care of by superiorization. A superiorized algorithm leads to images with the value of a secondary criterion (a merit function such as the total variation) improved as compared to images with similar data-inconsistency obtained by the algorithm without superiorization. Numerical experimentation shows that SupPCG can lead to high-quality reconstructions within a remarkably short time. A theoretical analysis is also provided.15A29, 65F22, 65F10, 94A08 Copyright © 2018, The Authors. All rights reserved.

关键词： Image reconstruction

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Meta-Learning for Optimization: A Case Study on the Flowshop Problem Using Decision Trees

Meta-Learning for Optimization: A Case Study on the Flowshop...

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Congress on Evolutionary Computation

作者： Lucas Pavelski Myriam Delgado Marie-Eleonore Kessaci Graduate Program in Electrical and Computer Engineering Federal University of Technology of Parana Curitiba Brazil DAINF/CPGEI Federal University of Technology of Parana Curitiba Brazil CRIStAL Universite de Lille CNRS UMR Lille France

This work investigates the use of meta-learning for optimization tasks when a classic operational research problem (flowshop) is considered at the base level. It involves sequencing a set of jobs to be processed by machines in series aiming to minimize the time spent. There are various algorithms or metaheuristics proposed to solve flowshop instances and the choice of the best one usually demands time and resources. Meta-learning applied to algorithm recommendation can simulate specialists' choices as it provides a mapping between the problem characteristics (called meta-features) and the algorithm performance. This work proposes an approach for knowledge discovery operating on the performance of four metaheuristics (Hill Climbing, Tabu Search, Simulated Annealing, and Iterated Local Search) while solving several flowshop instances. Besides recommending the best metaheuristic for each instance, the proposed approach can also recommend well suited parameters values using an Irace-based training process. Despite the possibility of using complex meta-features and powerful machine learning technique, the first experiments have been conducted using simple low and high-level meta-features and a classic machine learning model called Classification and Regression Trees (CART) for the recommendation. Results show that the proposed approach is promising as the induced rules indicate that some metaheuristic parameters are preferable. Nevertheless, regarding the algorithm recommendation there is a lot of room for improvement.

关键词： Optimization Task analysis Search problems Approximation algorithms Decision trees Electronic mail Sequential analysis

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