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Scientific reports 2020年第1期10卷 17120页

作者： Lize De Coster Pablo Sánchez-Herrero Carlos Aliaga Miguel A Otaduy Jorge López-Moreno Ana Tajadura-Jiménez DEI Interactive Systems Group Department of Computer Science and Engineering Universidad Carlos III de Madrid Avenida de la Universidad 30 28911 Leganés Madrid Spain. lcoster@inf.uc3m.es. Seddi Labs Madrid Spain. Modeling and Virtual Reality Group Department of Computer Science Universidad Rey Juan Carlos Madrid Spain. Multimodal Simulation Lab Department of Computer Science and Architecture Computer Systems and Languages Statistics and Operative Investigation Universidad Rey Juan Carlos Madrid Spain. DEI Interactive Systems Group Department of Computer Science and Engineering Universidad Carlos III de Madrid Avenida de la Universidad 30 28911 Leganés Madrid Spain.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Shape robust text detection with progressive scale expansion network

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arXiv 2019年

作者： Wang, Wenhai Xie, Enze Li, Xiang Hou, Wenbo Lu, Tong Yu, Gang Shao, Shuai National Key Lab for Novel Software Technology Nanjing University Department of Comuter Science and Technology Tongji University School of Computer and Engineering Nanjing University of Science and Technology Momenta Technology Inc PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Scene text detection has witnessed rapid progress especially with the recent development of convolutional neural networks. However, there still exists two challenges which prevent the algorithm into industry applications. On the one hand, most of the state-of-art algorithms require quadrangle bounding box which is in-accurate to locate the texts with arbitrary shape. On the other hand, two text instances which are close to each other may lead to a false detection which covers both instances. Traditionally, the segmentation-based approach can relieve the first problem but usually fail to solve the second challenge. To address these two challenges, in this paper, we propose a novel Progressive Scale Expansion Network (PSENet), which can precisely detect text instances with arbitrary shapes. More specifically, PSENet generates the different scale of kernels for each text instance, and gradually expands the minimal scale kernel to the text instance with the complete shape. Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances. Extensive experiments on CTW1500, Total-Text, ICDAR 2015 and ICDAR 2017 MLT validate the effectiveness of PSENet. Notably, on CTW1500, a dataset full of long curve texts, PSENet achieves a F-measure of 74.3% at 27 FPS, and our best F-measure (82.2%) outperforms state-of-art algorithms by 6.6%. The code will be released in the future. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical detection

ν-SVR polynomial kernel for predicting the defect density in new software projects

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arXiv 2018年

作者： López-Martín, Cuauhtémoc Azzeh, Mohammad Nassif, Ali Bou Banitaan, Shadi Department of Information Systems Universidad de Guadalajara Mexico Department of Software Engineering Applied Science Private University Amman Jordan Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States

An important product measure to determine the effectiveness of software processes is the defect density (DD). In this study, we propose the application of support vector regression (SVR) to predict the DD of new software projects obtained from the International software Benchmarking Standards Group (ISBSG) Release 2018 data set. Two types of SVR (i.e., Ε-SVR and ν-SVR) were applied to train and test these projects. Each SVR used four types of kernels. The prediction accuracy of each SVR was compared to that of a statistical regression (i.e., a simple linear regression, SLR). Statistical significance test showed that ν-SVR with polynomial kernel was better than that of SLR when new software projects were developed on mainframes and coded in programming languages of third generation. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

The MOOC Offer in Open Education: A Revision of the First IEEE Education Society' MOOC

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The MOOC Offer in Open Education: A Revision of the First IE...

Learning With MOOCS (LWMOOCS)

作者： Edmundo Tovar Rosa Cabedo Ahmad Alzaghoul Sergio Martín Martín Llamas Manuel Caeiro Oscar Martínez Bonastre Rebecca Strachan Manuel Castro Department of Computer Languages and Systems and Software Engineering Universidad Politécnica de Madrid Madrid Spain Department of Electrical Electronical and Control Engineering National Distance Education University Madrid Spain Department of Telematic Engineering University of Vigo Vigo Spain Department of Statistics Miguel Hernández University Elche Spain Department of Computer and Information Sciences Northumbria University Newcastle United Kingdom Department of Electrical and Computer Engineering National Distance Education University Madrid Spain

The following topics are dealt with: educational courses; computer aided instruction; Internet; further education; educational institutions; teaching; distance learning; mobile computing; learning (artificial intellig... 详细信息

ISBN: (纸本)9781538665343

关键词： Open Educational Resources Licenses Industries computer languages software engineering Europe

Quantum cognition models of ethical decision-making in human behavior simulation 29

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Quantum cognition models of ethical decision-making in human...

29th European Modeling and Simulation Symposium, EMSS 2017

作者： Yilmaz, Levent Department of Computer Science and Software Engineering Department of Industrial and Systems Engineering Samuel Ginn College of Engineering Auburn University United States

ISBN: (纸本)9781510847651

Ethical decision-making is a unique aspect of human behavior. When confronted with situations that require careful deliberation over multitude of options that have ethical implications, human behavior tends to resolve dilemmas by resorting to a range of heuristics and principles that view the situation from different perspectives. While constraint and utility-driven decision-making strategies are relevant, the incompatibility among these perspectives can invalidate the underlying premises of models of probabilistic utility-based decisions that rely on classic Kolmogorov axioms. In this paper, it is posited that quantum cognition models can provide an alternative and credible representation of human behavior modeling in simulations that involve ethical decision-making.

关键词： Decision making

ASD: Emotions and behavioural-based interventions through social sensor networks

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International Journal of Networking and Virtual Organisations 2018年第2-4期19卷 133-152页

作者： Faustin, Uwizeyimana Kovalan, A. Kannan, Rajkumar School of Computer Sciences and Application Periyar Maniyammai University Thanjavur India Department of Software Engineering Periyar Maniyammai University Thanjavur India Department of Information Systems College of Computer Sciences and IT King Faisal University Saudi Arabia

In India, nowadays one per 88 children is born with autism spectrum disorder (ASD) contrary to a ratio of one per 110 children few years back. In this paper, we propose a social sensor network for individuals with ASD to offer medical promotions as intervention and first aid. They should be in care and medical surveillances. We are monitoring enjoyment, frustration, boredom, and stress by using body-worn sensors and video streaming. This sensor collects the information from the human body and transmits it to their smart phones then to the external server. We introduce an algorithm named as spectrum clustering algorithm (SCA) which clusters people based on activities of the child. Communication is provided between the clusters of people by using context recommendation algorithm. We use prerogative response algorithm (PRA) for load balancing on server while processing the requests. Our processes are simulated using Omnet++ simulator and provide effective results. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Sensor networks

AI-skin: Skin disease recognition based on self-learning and wide data collection through a closed loop framework

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arXiv 2019年

作者： Chen, Min Zhou, Ping Wu, Di Hu, Long Hassan, Mohammad Mehedi Alamri, Atif School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Chair of Pervasive and Mobile Computing Software Engineering Department CCIS King Saud University Riyadh11543 Saudi Arabia

There are a lot of hidden dangers in the change of human skin conditions, such as the sunburn caused by long-time exposure to ultraviolet radiation, which not only has aesthetic impact causing psychological depression and lack of selfconfidence, but also may even be life-threatening due to skin canceration. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. However, the excessive dependence on the image sample database is unable to provide individualized diagnosis service for different population groups. To overcome this problem, a medical AI framework based on data width evolution and self-learning is put forward in this paper to provide skin disease medical service meeting the requirement of real time, extendibility and individualization. First, the wide collection of data in the close-loop information flow of user and remote medical data center is discussed. Next, a data set filter algorithm based on information entropy is given, to lighten the load of edge node and meanwhile improve the learning ability of remote cloud analysis model. In addition, the framework provides an external algorithm load module, which can be compatible with the application requirements according to the model selected. Three kinds of deep learning model, i.e. LeNet-5, AlexNet and VGG16, are loaded and compared, which have verified the universality of the algorithm load module. The experiment platform for the proposed real-time, individualized and extensible skin disease recognition system is built. And the system's computation and communication delay under the interaction scenario between tester and remote data center are analyzed. It is demonstrated that the system we put forward is reliable and effective. Copyright © 2019, The Authors. All rights reserved.

关键词： Data acquisition

Green Energy Sources: Issues and Challenges

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Green Energy Sources: Issues and Challenges

Roedunet International Conference (RoEduNet)

作者： Mostafa Abdulghafoor Mohammed Inas Adnan Mohammed Raed.A. Hasan Nicolae Ţăpuş Ahmed Hussien Ali Omar A. Hammood Faculty of Automatic Control and Computers University Polytechnic of Bucharest/Romania The Great Emam University College Baghdad University of Information Technology and Communications / college of Engineering/ Baghdad Iraq Faculty of Al-dour Technical institute Northern Technical University Mosel IRAQ Faculty of Automatic Control and Computers University Polytechnic of Bucharest/Romania Computer Science Department AL Salam University College Baghdad Iraq Faculty of Computer Systems and Software Engineering UniversityMalaysia Pahang Malaysia

Mobile offloading is a platform that facilitates the distribution of computationally intensive tasks from mobile devices to the cloud or other devices in order to conserve energy and improve performance. The concept is practically based on the idea of exchanging relatively low communication energy for high computation power utilization. Efforts have been channeled towards energy conservation by clusters using Dynamic Power Management (DPM). This paper is a review for green energy sources for reducing energy and using energy consumption to manage sources by stating the latest studies of energy consumption and energy consumptions.

关键词： Energy consumption Task analysis Servers Energy efficiency Power system management Voltage control Cloud computing

Correction to: Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

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Archives of Computational Methods in engineering 2021年第7期28卷 5039-5039页

作者： Ismail, Leila Materwala, Huned Tayefi, Maryam Ngo, Phuong Karduck, Achim P. Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Norwegian Centre for E-Health Research Tromsø Norway Faculty of Informatics Furtwangen University Furtwangen Germany

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning