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Responsible urban innovation with local government artificial intelligence (Ai): A conceptual framework and research agenda

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Journal of Open Innovation: Technology, Market, and Complexity 2021年第1期7卷 1-16页

作者： Yigitcanlar, Tan Corchado, Juan M. Mehmood, Rashid Li, Rita Yi Man Mossberger, Karen Desouza, Kevin School of Architecture and Built Environment Queensland University of Technology 2 George Street Brisbane 4000 QLD Australia School of Technology Federal University of Santa Catarina Campus Universitario Trindade Florianópolis SC 88040-900 Brazil Bisite Research Group University of Salamanca Salamanca 37007 Spain Air Institute IoT Digital Innovation Hub Salamanca 37188 Spain Department of Electronics Information and Communication Faculty of Engineering Osaka Institute of Technology Osaka 535-8585 Japan High Performance Computing Center King Abdulaziz University Al Ehtifalat St Jeddah 21589 Saudi Arabia Department of Economics and Finance Hong Kong Shue Yan University 10 Wai Tsui Cres North Point Hong Kong School of Public Affairs Arizona State University 411 North Central Avenue Phoenix 85004 AZ United States School of Management Queensland University of Technology 2 George Street Brisbane 4000 QLD Australia

The urbanization problems we face may be alleviated using innovative digital technology. However, employing these technologies entails the risk of creating new urban problems and/or intensifying the old ones instead of alleviating them. Hence, in a world with immense technological opportunities and at the same time enormous urbanization challenges, it is critical to adopt the principles of responsible urban innovation. These principles assure the delivery of the desired urban outcomes and futures. We contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework. In this perspective paper, we advocate for the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems in order to achieve responsible urban innovation. The statements made in this perspective paper are based on a thorough review of the literature, research, developments, trends and applications carefully selected and analyzed by an expert team of investigators. This study provides new insights, develops a conceptual framework and identifies prospective research questions by placing local government AI systems under the microscope through the lens of responsible urban innovation. The presented overview and framework, along with the identified issues and research agenda, offer scholars prospective lines of research and development;where the outcomes of these future studies will help urban policymakers, managers and planners to better understand the crucial role played by local government AI systems in ensuring the achievement of responsible outcomes. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： AI revolution Artificial intelligence (AI) Community engagement Local government AI Responsible AI Responsible innovation Responsible urban innovation Smart city Urban policy Urban technology

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Web Ranking of Higher Education Institutions: An SEO Analysis

Web Ranking of Higher Education Institutions: An SEO Analysi...

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International Conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Alrence Santiago Halibas Anju Matthew Cherian Indu Govinda Pillai Leslyn Bonachita Reazol Erbeth Gerald Delvo Genevive Halasan Sumondong Faculty of Computing Sciences Gulf College Muscat Oman College of Engineering and Architecture La Salle University Ozamiz City PHL Ozamiz City School of Arts and Trades Ozamiz City PHL

ISBN: (数字)9781728106663

ISBN: (纸本)9781728106670

There are numerous ranking systems that provide web rankings for colleges and universities around the world. The aim of this paper is to present the findings of SEO audit and analysis of 34 HEI websites in Oman in terms of web presence and impact. The study used the SEO audit metrics-domain authority score, linking root domain, ranking keywords, and spam score which are provided by Moz. The findings revealed that only one (1) HEI has achieved a good quality for domain authority while all the others have obtained a low quality only for domain authority. The same HEI has scored high on the linking root domains and ranking keywords. However, 24 % of the HEIs have attained a zero spam score while 76% of the HEIs have attained a low spam score. The study recommends that HEIs should exert more efforts in improving their SEO to increase visibility and impact on the web.

关键词： auditing educational institutions further education Internet optimisation search engines security of data social networking (online) unsolicited e-mail Web sites Unsolicited electronic mail Educational institutions audit Continuing Education Data Security websites Search engines Web search engines Internets World Wide Web congenital web webs higher education institute

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Predicting the governing factors for the release of colloidal phosphorus using machine learning

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Chemosphere 2024年 362卷 142699页

作者： Khan, Sangar Gao, Huimin Milham, Paul Eltohamy, Kamel Mohamed Ullah, Habib Mu, Hongli Gao, Meixiang Yang, Xiaodong Hamid, Yasir Hooda, Peter S. Shaheen, Sabry M. Wu, Naicheng Department of Geography and Spatial Information Techniques Ningbo University Ningbo315211 China Donghai Institute Ningbo University Ningbo315211 China Zhejiang Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research Ningbo University Ningbo315211 China Hawkesbury Institute for the Environment University of Western Sydney LB 1797 PenrithNSW2751 Australia College of Environmental Resource Sciences Zhejiang University Hangzhou310058 China Water Relations and Field Irrigation Department Agricultural and Biological Research Division National Research Centre Cairo 12622 Egypt Innovation Center of Yangtze River Delta Zhejiang University Zhejiang 311400 China Faculty of Engineering Computing and the Environment Kingston University London United Kingdom University of Wuppertal School of Architecture and Civil Engineering Laboratory of Soil Groundwater Management Pauluskirchstraße 7 Wuppertal42285 Germany King Abdulaziz University Faculty of Environmental Sciences Department of Agriculture Jeddah21589 Saudi Arabia University of Kafrelsheikh Faculty of Agriculture Department of Soil and Water Sciences Kafr El-Sheikh33516 Egypt Institute of Hydraulic and Ocean Engineering Ningbo315211 China

Predicting the parameters that influence colloidal phosphorus (CP) release from soils under different land uses is critical for managing the impact on water quality. Traditional modeling approaches, such as linear regression, may fail to represent the intricate relationships that exist between soil qualities and environmental influences. Therefore, in this study, we investigated the major determinants of CP release from different land use/types such as farmland, desert, forest soils, and rivers. The study utilizes the structural equation model (SEM), multiple linear regression (MLR), and three machine learning (ML) models (Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost)) to predict the release of CP from different soils by using soil iron (Fe), aluminum (Al), calcium (Ca), pH, total organic carbon (TOC) and precipitation as independent variables. Results show that colloidal-cations (Fe, Al, Ca) and colloidal-TOC strongly influence CP release, while bioclimatic variables (precipitation) and pH have weaker effects. XGBoost outperforms the other models with an R2 of 0.94 and RMSE of 0.09. SHapley Additive Explanations described the outcomes since XGBoost is accurate. The relative relevance ranking indicated that colloidal TOC had the highest ranking in predicting CP. This was supported by the analysis of partial dependence plots, which showed that an increase in colloidal TOC increased soil CP release. According to our research, the SHAP XGBoost model provides significant information that can help determine the variables that considerably influence CP contents as compared to RF, SVM, and MLR. © 2024 Elsevier Ltd

关键词： Multiple linear regression

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The Needs, Challenges, and Priorities for Advancing Global Flood Research

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Wiley Interdisciplinary Reviews: Water 2025年第3期12卷

作者： Samadi, Vidya Fowler, Hayley J. Lamond, Jessica Wagener, Thorsten Brunner, Manuela Gourley, Jonathan Moradkhani, Hamid Popescu, Ioana Wasko, Conrad Wright, Daniel Wu, Huan Zhang, Ke Arias, Paola A. Duan, Qingyun Nazemi, Ali van Oevelen, Peter J. Prein, Andreas F. Roundy, Joshua K. Saberian, Mostafa Umutoni, Lisa Agricultural Sciences Department Clemson University Clemson SC United States Artificial Intelligence Research Institute for Science and Engineering (AIRISE) School of Computing Clemson University Clemson SC United States School of Engineering and Tyndall Centre for Climate Change Research Newcastle University Newcastle upon Tyne United Kingdom Centre for Architecture and Built Environment Research University of the West of England Bristol United Kingdom Institute of Environmental Science and Geography University of Potsdam Potsdam Germany Institute for Atmospheric and Climate Science ETH Zurich Zurich Switzerland WSL Institute for Snow and Avalanche Research SLF Davos Dorf Switzerland Climate Change Extremes and Natural Hazards in Alpine Regions Research Center CERC Davos Dorf Switzerland NOAA/National Severe Storms Laboratory Norman OK United States Center for Complex Hydrosystems Research and Department of Civil Construction and Environmental Engineering The University of Alabama Tuscaloosa AL United States IHE Delft Institute for Water Education Delft Netherlands Faculty of Civil Engineering and Geosciences Delft University of Technology Delft Netherlands School of Civil Engineering The University of Sydney Sydney NSW Australia Civil and Environmental Engineering University of Wisconsin-Madison Madison WI United States Southern Marine Science and Engineering Laboratory and School of Atmospheric Sciences Sun Yat-Sen University Guangdong China College of Hydrology and Water Resources Hohai University Jiangsu Nanjing China GIGA Group School of Environment University of Antioquia Antioquia Colombia Center for Hydrometeorological Predictions College of Hydrology and Water Resources Hohai University Jiangsu Nanjing China Department of Building Civil and Environmental Engineering Concordia University Montreal QC Canada International GEWEX Project Office Fairfax VA United States Department of Atmospheric Oceanic and Earth Sciences George Mason

In recent years, numerous flood events have caused loss of life, widespread disruption, and damage across the globe. These devastating impacts highlight the importance of a better understanding of flood generating processes, their impacts, and their variability under climate and landscape changes. Here, we argue that the ability to better model flooding is underpinned by the grand challenge of understanding flood generation mechanisms and potential impacts. To address this challenge, the World Meteorological Organization-Global Energy and Water Exchanges (GEWEX) Hydrometeorology Panel (GHP) aims to establish a Global Flood Crosscutting project to propagate flood modeling and research knowledge across regions and to synthesize results at the global scale. This paper outlines a framework for understanding the dynamics and impacts of runoff generation processes and a rationale for the role of a Global Flood Crosscutting project to address these challenges. Within this Global Flood Crosscutting project, we will establish a common terminology and methods to enable the global research community to exchange knowledge and experiences, and to design experiments toward developing actionable recommendations for more effective flood management practices and policies for improved resilience. This harmonization of rich perspectives across disciplines will foster the co-production of knowledge primed to advance flood research, particularly in the current period of heightened climate variability and rapid change. It will create a new transdisciplinary paradigm for flood science, wherein different dimensions of mechanistic understanding and processes are rigorously considered alongside socioeconomic impacts, early warning communications, and longer-term adaptation to alleviate flood risks in society. © 2025 The Author(s). WIREs Water published by Wiley Periodicals LLC.

关键词： effective flood management flood mechanistic understanding flood modeling GEWEX knowledge exchange

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Comparative analysis of data based on the WHO cancer country profiles on breast cancer and other common cancers globally, in the UK and Saudi Arabia

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AIP Conference Proceedings 2023年第1期2872卷

作者： Mohammed Erkhawan Hameed Rasheed Mansour Youseffi Luca Parisi Farideh Javid Saeed Afshin Javid Faculty of Engineering and Informatics University of Bradford Bradford BD7 1DP United Kingdom School of Applied Sciences Pharmacy University of Huddersfield Huddersfield HD1 3DH United Kingdom School of Architecture Computing and Engineering (ACE) University of East London London E16 2RD United Kingdom

This study summarises the available data according to the World Health Organization (WHO) Cancer Country Profiles for the status of breast cancer and other common types of cancer globally, in addition to the UK and Saudi Arabia. The data presented consists of the total population in each case, as well as the total number of cancer cases and the total number of deaths due to cancer. Moreover, we report the incidence and mortality of the most common types of cancer, along with the population attributable fractions (PAFs), explaining the contribution of selected risk factors that are essential to understand the aetiology and occurrence of cancer worldwide.

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Explicit high-order generalized-α methods for isogeometric analysis of structural dynamics

arXiv

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arXiv 2021年

作者： Behnoudfar, Pouria Loli, Gabriele Reali, Alessandro Sangalli, Giancarlo Calo, Victor M. Curtin Institute for Computation School of Earth and Planetary Sciences Curtin University Kent Street Bentley PerthWA6102 Australia Dipartimento di Matematica "F. Casorati" Università di Pavia Pavia27100 Italy Department of Civil Engineering and Architecture Università di Pavia Pavia27100 Italy School of Electrical Engineering Computing and Mathematical Sciences Curtin University P.O. Box U1987 PerthWA6845 Australia

We propose a new family of high-order explicit generalized-α methods for hyperbolic problems with the feature of dissipation control. Our approach delivers 2k, (k ∈ N) accuracy order in time by solving k matrix systems explicitly and updating the other 2k variables at each time-step. The user can control the numerical dissipation in the discrete spectrum’s high-frequency regions by adjusting the method’s coefficients. We study the method’s spectrum behaviour and show that the CFL condition is independent of the accuracy order. The stability region remains invariant while we increase the accuracy order. Next, we exploit efficient preconditioners for the isogeometric matrix to minimize the computational cost. These preconditioners use a diagonal-scaled Kronecker product of univariate parametric mass matrices;they have a robust performance with respect to the spline degree and the mesh size, and their decomposition structure implies that their application is faster than a matrix-vector product involving the fully-assembled mass matrix. Our high-order schemes require simple modifications of the available implementations of the generalized-α method. Finally, we present numerical examples demonstrating the methodology’s performance regarding single- and multi-patch IGA discretizations. © 2021, CC BY.

关键词： Structural dynamics

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Normal brain MRI of a 40-year-old male: A case study

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AIP Conference Proceedings 2023年第1期2872卷

作者： Mohammed Erkhawan Hameed Rasheed Mansour Youseffi Luca Parisi Saeed Afshin Javid Farideh Javid Faculty of Engineering and Informatics University of Bradford Bradford BD7 1DP United Kingdom School of Architecture Computing and Engineering (ACE) University of East London London E16 2RD United Kingdom School of Applied Sciences Pharmacy University of Huddersfield Huddersfield HD1 3DH United Kingdom

Magnetic resonance imaging (MRI) is one of the medical imaging modalities that is most often used to examine the brain, especially considering that it is a non-ionising technique [1]. With the aid of various MRI sequences, such as T1-weighted, T2-weighted, or fluid attenuated inversion recovery (FLAIR), brain MRI can provide detailed three-dimensional images of the brain used to analyse the anatomy and pathology of the various regions of the brain [2]. Thus, MRI is a safe, painless, and non-invasive medical imaging technique [3]; however, it may take longer to perform a brain scan, as compared to other medical imaging modalities, such as Computed Tomography (CT) [4], although CT is a technique that leverages ionising radiation. This case study describes the case of a 40-year-old male with migraine, including an analysis of his medical history and MRI images. The MRI images showed a physiological brain with no suspicious intracranial lesions.

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To talk or to work: Flexible communication compression for energy efficient federated learning over heterogeneous mobile edge devices

arXiv

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arXiv 2020年

作者： Li, Liang Shi, Dian Hou, Ronghui Li, Hui Pan, Miao Han, Zhu School of Cyber Engineering Xidian University Xi’an China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Department of Electrical and Computer Engineering University of Houston HoustonTX United States

Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications. Despite the potential benefits, FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training, raising great challenges to battery constrained mobile devices. In this work, we target at improving the energy efficiency of FL over mobile edge networks to accommodate heterogeneous participating devices without sacrificing the learning performance. To this end, we develop a convergence-guaranteed FL algorithm enabling flexible communication compression. Guided by the derived convergence bound, we design a compression control scheme to balance the energy consumption of local computing (i.e., "working") and wireless communication (i.e., "talking") from the long-term learning perspective. In particular, the compression parameters are elaborately chosen for FL participants adapting to their computing and communication environments. Extensive simulations are conducted using various datasets to validate our theoretical analysis, and the results also demonstrate the efficacy of the proposed scheme in energy saving. © 2020, CC BY.

关键词： Wireless networks

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FTT-NAS: Discovering fault-tolerant convolutional neural architecture

arXiv

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arXiv 2020年

作者： Ning, Xuefei Ge, Guangjun Li, Wenshuo Zhu, Zhenhua Zheng, Yin Chen, Xiaoming Gao, Zhen Wang, Yu Yang, Huazhong Department of Electronic Engineering Tsinghua University China Weixin Group Tencent China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China School of Electrical and Information Engineering Tianjin University China

With the fast evolvement of embedded deep-learning computing systems, applications powered by deep learning are moving from the cloud to the edge. When deploying neural networks (NNs) onto the devices under complex environments, there are various types of possible faults: soft errors caused by cosmic radiation and radioactive impurities, voltage instability, aging, temperature variations, malicious attackers, etc. Thus the safety risk of deploying NNs is now drawing much attention. In this paper, after the analysis of the possible faults in various types of NN accelerators, we formalize and implement various fault models from the algorithmic perspective. We propose Fault-Tolerant Neural architecture Search (FT-NAS) to automatically discover convolutional neural network (CNN) architectures that are reliable to various faults in nowadays devices. Then we incorporate fault-tolerant training (FTT) in the search process to achieve better results, which is referred to as FTT-NAS. Experiments on CIFAR-10 show that the discovered architectures outperform other manually designed baseline architectures significantly, with comparable or fewer floating-point operations (FLOPs) and parameters. Specifically, with the same fault settings, F-FTT-Net discovered under the feature fault model achieves an accuracy of 86.2% (VS. 68.1% achieved by MobileNet-V2), and W-FTT-Net discovered under the weight fault model achieves an accuracy of 69.6% (VS. 60.8% achieved by ResNet-18). By inspecting the discovered architectures, we find that the operation primitives, the weight quantization range, the capacity of the model, and the connection pattern have influences on the fault resilience capability of NN models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Highly efficient breadth-first search on CPU-based single-node system 21

Highly efficient breadth-first search on CPU-based single-no...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhang, Chenglong Cao, Huawei Ye, Xiaochun Wang, Guobo Hao, Qinfen Fan, Dongrui State Key' Laboratoiy of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781728120584

With the rapid development of computer technology, the scale of graph increases explosively and large-scale graph computing has been the focus in recent years. Breadth-First Search (BFS) is one of the most important kernels in graph computing. It is the main kernel of the Graph500 benchmark that evaluates the performance of supercomputers and servers in terms of data-intensive applications. In this paper, we implement a highly efficient parallel BFS on CPU-based single-node systems. We propose two algorithm optimization techniques: round-robin vertex shuffle and jumping access. The former can improve the load balance for parallel implementation, while the latter can reduce unnecessary data accesses in the bottom-up algorithm. Our new implementation demonstrates strong scalability and achieves 65.2 GTEPS for Kronecker graph with 2 30 vertices and 2 34 edges on a 4-way Xeon server. The speedup is nearly 12.8 times faster than the original direction-optimizing algorithm. In terms of energy efficiency, the result on our self-designed ARM server is 254.6 MTEPS/W and is superior to conventional x86 server. To our best knowledge, this is the first work that evaluates BFS performance on ARM server and ARM server is suitable for data-intensive applications such as large-scale graph processing. © 2019 IEEE.

关键词： Supercomputers

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