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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Haoge Wang Quanyu Wang Wenming Wang Software Architecture and Big Data dept. of Computer Science Beijing Institute of Technology Beijing China

With the development of the information era, the application of big data technology in education has become more and more extensive. This article adopts the Hadoop platform to conduct parallel mining of educational literature on big data. The paper has analyzed the main function of text mining technology, combined Canopy and K-Means algorithm to analyze and research the educational big data literature, learned about the application of big data technology in the field of education in order to provide its services for the construction of "World-class universities and first-class disciplines" and make a contribution to the development of education.

关键词： Big Data Education Text mining Clustering algorithms Computational modeling Software architecture

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SMART project management for SMART cities: Analyzing critical factors affecting trust among Virtual Project Teams

SMART project management for SMART cities: Analyzing critica...

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HCT Information Technology Trends (ITT)

作者： Sukhwant Kaur Vishwesh Akre Mohammed Arif University of Ontario Institute of Technology Durham College-School of Business IT and Technology Ontario Canada Computer and Information Science Higher Colleges of Technology Dubai Women's Dubai United Arab Emirates Architecture and Built Environment Professor of Sustainability and Construction Futures Faculty of Science and Engineering University of Wolverhampton United Kingdom

ISBN: (数字)9781728150611

ISBN: (纸本)9781728150628

SMART cities suggest a domain that attracts an increasing scientific, political and economic attention. Smart Cities have been characterised and defined by a number of factors including sustainability, economic development and a high quality of life. Enhancing these factors can be achieved through infrastructure (physical capital), human capital, social capital and ICT infrastructure. It requires ambitious projects to transform entire urban areas through master planning and infrastructure development. This required SMART project management. In order to accomplish the goals of SMART project management, the construction industries have to rely on multi -disciplinary project teams having varied expertise. Majority of these companies have adopted virtual teams for their business activities. These virtual teams work at global level and face a number of challenges that can have an impact on team effectiveness. This research paper focuses on classification of smart cities and key challenges faced by the virtual project teams. Further to this, this paper reviews existing international literature to identify the variables affecting trust among virtual team members in construction sector of Middle East. The variables are then grouped together to form factors after understanding their definitions and commonalities among them. This research highlights the importance of these factors to project managers of smart city projects and it would be beneficial to Project Managers of Architectural/Engineering/Construction Companies as well as that of other sectors by facilitating them to know the actions required for better team collaboration in virtual teams. This may lead to greater team performance and individual learning.

关键词： Virtual groups Smart cities Task analysis Project management Companies Cultural differences Training

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Stability selection enables robust learning of partial differential equations from limited noisy data

arXiv

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arXiv 2019年

作者： Maddu, Suryanarayana Cheeseman, Bevan L. Sbalzarini, Ivo F. Müller, Christian L. Chair of Scientific Computing for Systems Biology Faculty of Computer Science TU Dresden Dresden01069 Germany Center for Systems Biology Dresden Max Planck Institute of Molecular Cluster of Excellence Physics of Life TU Dresden Dresden01062 Germany Center for Computational Mathematics Flatiron Institute New York United States

We present a statistical learning framework for robust identification of partial differential equations (PDEs) from noisy spatiotemporal data. Extending previous sparse regression approaches for inferring PDE models from simulated data, we address key issues that have thus far limited the application of these methods to noisy experimental data, namely their robustness against noise and the need for manual parameter tuning. We address both points by proposing a stability-based model selection scheme to determine the level of regularization required for reproducible recovery of the underlying PDE. This avoids manual parameter tuning and provides a principled way to improve the method's robustness against noise in the data. Our stability selection approach, termed PDE-STRIDE, can be combined with any sparsity-promoting penalized regression model and provides an interpretable criterion for model component importance. We show that in particular the combination of stability selection with the iterative hard-thresholding algorithm from compressed sensing provides a fast, parameter-free, and robust computational framework for PDE inference that outperforms previous algorithmic approaches with respect to recovery accuracy, amount of data required, and robustness to noise. We illustrate the performance of our approach on a wide range of noise-corrupted simulated benchmark problems, including 1D Burgers, 2D vorticity-transport, and 3D reaction-diffusion problems. We demonstrate the practical applicability of our method on real-world data by considering a purely data-driven re-evaluation of the advective triggering hypothesis for an embryonic polarization system in C. elegans. Using fluorescence microscopy images of C. elegans zygotes as input data, our framework is able to recover the PDE model for the regulatory reaction-diffusion-flow network of the associated proteins. Copyright © 2019, The Authors. All rights reserved.

关键词： Stability

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Automated prediction of shopping behaviours using taxi trajectory data and social media reviews 3

Automated prediction of shopping behaviours using taxi traje...

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3rd IEEE International Conference on Big Data Analysis, ICBDA 2018

作者： Gong, Shuhui Cartlidge, John Bai, Ruibin Yue, Yang Li, Qingquan Qiu, Guoping International Doctoral Innovation Centre Department of Computer Science University of Nottingham Ningbo China Ningbo315100 China Shenzhen Key Laboratory of Spatial Smart Sensing and Services Research Institute for Smart Cities Department of Urban Informatics School of Architecture and Urban Planning Shenzhen University Shenzhen518060 China Department of Computer Science University of Bristol BristolBS8-1UB United Kingdom School of Computer Science University of Nottingham NottinghamNG8-1BB United Kingdom

ISBN: (纸本)9781538647936

The Huff model is a well used mathematical abstraction for predicting shopping centre patronage. It considers two factors: shopping centre attractiveness, and customers' travel costs. Here, taxi trajectory data (more than three million journeys) and social media data (more than eight thousand customer reviews) is used to calibrate the Huff model for five primary shopping centres in the rapidly expanding metropolitan city of Shenzhen, China. The Huff model is calibrated in two ways: globally, to find the single pair of best-fit parameters for attractiveness and travel cost;and locally, using Geographical Weighted Regression to find the best-fit parameters at each spatial location. Results demonstrate that customer reviews on social media provide relatively high prediction accuracy for weekend shopping behaviours when the Huff model is calibrated globally. In contrast, customer footfall, calculated directly from number of taxi journeys, provides higher prediction accuracy when the Huff model is calibrated locally. This suggests that, at weekends, sensitivity to footfall has greater spatial variance (i.e., customers living in some areas have greater preference for shopping at popular centres) than sensitivity to customer reviews (i.e., regardless of where customers live, positive reviews on social media are equally likely to affect behaviour). We present this geographical homogeneity in review sensitivity and heterogeneity in footfall sensitivity as a novel discovery with potential applications in urban, retail, and transportation planning. © 2018 IEEE.

关键词： Forecasting

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Using the Model of a Functional Rationalizer of Consumer Behavior in Recommendation Systems for Managing the Transport Activity of the Urban Population

Using the Model of a Functional Rationalizer of Consumer Beh...

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International Conference on Soft Computing and Measurements (SCM)

作者： Yaroslav A. Seliverstov Svytoslav A. Seliverstov Nikolay V. Podoprigora Alexey L. Starichenkov Roman S. Naryshkin Laboratory of Intelligent Transport Systems Solomenko Institute of Transport Problems of the Russian Academy of Sciences Institute of Computer Science and Technology Peter the Great St.Petersburg Polytechnic University St. Petersburg Russian Federation Department of Ground Transportation Technological Machines Saint Petersburg State University of Architecture and Civil Engineering St. Petersburg Russian Federation Department of Ship Control Systems Saint Petersburg Electrotechnical University “LETI” St. Petersburg Russian Federation Department of Programming Technology Saint Petersburg State University St. Petersburg Russian Federation

ISBN: (数字)9781728196923

ISBN: (纸本)9781728196930

An overview of existing urban software mobile applications of the transport and economic direction is given. A model of a functional rationalizer of consumer behavior is being built. The software model of the functional rationalizer of consumer behavior (SM FRCB) was developed for the first time. PMFRCB allows the user to expand the number of his preferred needs (occupations, activities) during the daily cycle through intelligent recommendations. Also, SM FRCB allows you to optimize needs (activities, activities) at the cost and rate of their use. The SM FRCB is made in Python_3 using the libraries matplotlib, collections, numpy, pyplot, mplot3d. The analysis of consumer behavior before and after using the functional rationalizer model is carried out. The graphical results of a structural analysis of the costs of old and new consumer behavior, analysis of consumer behavior within the boundaries of the theory of possibilities and the model of transport behavior of a resident before and after applying the functional rationalizer model are presented. The prospects of further improving the SM FRCB are considered.

关键词： Consumer behavior Mobile applications Sociology Statistics Logistics Analytical models Software

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Spatio-temporal prediction of shopping behaviours using taxi trajectory data 3

Spatio-temporal prediction of shopping behaviours using taxi...

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3rd IEEE International Conference on Big Data Analysis, ICBDA 2018

作者： Cartlidge, John Gong, Shuhui Bai, Ruibin Yue, Yang Li, Qingquan Qiu, Guoping Department of Computer Science University of Bristol BristolBS8-1UB United Kingdom International Doctoral Innovation Centre Department of Computer Science University of Nottingham Ningbo China Ningbo315100 China Shenzhen Key Laboratory of Spatial Smart Sensing and Services Research Institute for Smart Cities Department of Urban Informatics School of Architecture and Urban Planning Shenzhen University Shenzhen518060 China School of Computer Science University of Nottingham NottinghamNG8-1BB United Kingdom

ISBN: (纸本)9781538647936

Taxi trajectory data (GPS data collected for 15,000 taxis at intervals of 30 seconds across three million journeys over eight days) is used to generate a spatio-temporal prediction of shopping behaviours in the emerging metropolitan city of Shenzhen, China. Two approaches are compared: time-series forecasting using ARIMA;and a gravity model approach, using the Huff model calibrated with Geographical Weighted Regression. Results demonstrate that ARIMA performs with significantly higher accuracy than the more traditional Huff model method. Further, it is demonstrate that while the accuracy of the Huff model is constrained by model assumptions, applying time-series methods to the underlying data directly (i.e., the ARIMA method) has no such constraints, and is limited only by the amount of data available. This suggests that, as richer data sets become available, spatio-temporal modelling of this kind will become more accurate. © 2018 IEEE.

关键词： Time series

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Extrapolating continuous color emotions through deep learning

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Physical Review Research 2020年第3期2卷 033350-033350页

作者： Vishaal Ram Laura P. Schaposnik Nikos Konstantinou Eliz Volkan Marietta Papadatou-Pastou Banu Manav Domicele Jonauskaite Christine Mohr Milton High School Milton Georgia 30004 USA Department of Mathematics Statistics and Computer Science University of Illinois Chicago Illinois 60607 USA Department of Rehabilitation Sciences Faculty of Health Sciences Cyprus University of Technology Limassol 3036 Cyprus Department of Psychology Cyprus International University Nicosia 99258 Cyprus National and Kapodistrian University of Athens Athens 157 72 Greece Kadir Has University Faculty of Art and Design Department of Interior Architecture and Environmental Design Kadir Has Caddesi 34083 Cibali-İstanbul Institute of Psychology University of Lausanne Lausanne 1015 Switzerland

By means of an experimental dataset, we use deep learning to implement an RGB (red, green, and blue) extrapolation of emotions associated to color, and do a mathematical study of the results obtained through this neural network. In particular, we see that males (type-m individuals) typically associate a given emotion with darker colors, while females (type-f individuals) associate it with brighter colors. A similar trend was observed with older people and associations to lighter colors. Moreover, through our classification matrix, we identify which colors have weak associations to emotions and which colors are typically confused with other colors.

关键词： Animal behavior Memory Speech

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Genetic algorithm optimized support vector machine in noma-based satellite networkswith imperfect csi

arXiv

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arXiv 2020年

作者： Yan, Xiaojuan An, Kang Wang, Cheng-Xiang Zhu, Wei-Ping Li, Yusheng Feng, Zhiqiang National Mobile Commun. Research Lab School of Info. Science and Eng. Southeast University Nanjing210096 China College of Mechanical Naval Architecture and Ocean Engineering Beibu Gulf University Qinzhou535011 China Sixty-third Research Institute National University of Defense Technology Nanjing210007 China Purple Mountain Laboratories Nanjing211111 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada

With the help of a power-domain non-orthogonal multiple access (NOMA) scheme, satellite networks can simultaneously serve multiple users within limited time/spectrum resource block. However, the existence of channel estimation errors inevitably degrade the judgment on users' channel state information (CSI) accuracy, thus affecting the user pairing processing and suppressing the superiority of the NOMA scheme. Inspired by the advantages ofmachine learning (ML) algorithms, we propose an improved support vector machine (SVM) scheme to reduce the inappropriate user pairing risks and enhance the performance of NOMA based satellite networks with imperfect CSI. Particularly, a genetic algorithm (GA) is employed to optimize the regularization and kernel parameters of the SVM, which effectively improves the classification accuracy of the proposed scheme. Simulations are provided to demonstrate that the performance of the proposed method is better than that with random user paring strategy, especially in the scenario with a large number of users. Copyright © 2020, The Authors. All rights reserved.

关键词： Genetic algorithms

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A java code protection scheme via dynamic recovering runtime instructions 18th

A java code protection scheme via dynamic recovering runtime...

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18th International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2018

作者： Jiajia, Sun Jinbao, Gao Yu-an, Tan Yu, Zhang Xiao, Yu School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China China University of Mining and Technology Beijing100083 China Department of Computer Science and Technology Shandong University of Technology ZiboShandong255022 China

ISBN: (纸本)9783030050627

As Android operating system and applications on the device play important roles, the security requirements of Android applications increased as well. With the upgrade of Android system, Android runtime mode (ART mode) has gradually become the mainstream architecture of the Android operating system. ART introduces several improvements in Android, but it also introduces new ways to enhance malicious activities. This paper proposed a confidential finer granularity protection scheme for application programs under ART mode of ROOT Android devices. Taking Java method as the protection granularity, the protection scheme increased the accuracy of protecting targets. In addition, the protection scheme provided a more thorough protection for applications by combining dynamic loading technology and encryption technology in ART mode, and improved the security of Android applications. Experiments showed that the proposed protection scheme is effective. © Springer Nature Switzerland AG 2018.

关键词： Android (operating system)

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OCR4all - An open-source tool providing a (semi-)automatic ocr workflow for historical printings

arXiv

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arXiv 2019年

作者： Reul, Christian Christ, Dennis Hartelt, Alexander Balbach, Nico Wehner, Maximilian Springmann, Uwe Wick, Christoph Grundig, Christine Büttner, Andreas Puppe, Frank Chair for Artificial Intelligence and Applied Computer Science University of Würzburg Würzburg97074 Germany Center for Information and Language Processing LMU Munich Munich80538 Germany Institute for Modern Art History University of Zurich Zurich8006 Switzerland Institute for Philosophy University of Würzburg Würzburg97074 Germany

Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout recognition and segmentation, character recognition and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper we present an open-source OCR software called OCR4all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. A comfortable GUI allows error corrections not only in the final output, but already in early stages to minimize error propagations. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. Experiments showed that users with minimal or no experience were able to capture the text of even the earliest printed books with manageable effort and great quality, achieving excellent character error rates (CERs) below 0.5%. The fully automated application on 19th century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. The architecture of OCR4all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical character recognition

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