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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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Xdn: towards efficient inference of residual neural networks on cambricon chips 2nd

Xdn: towards efficient inference of residual neural networks...

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2nd International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019

作者： Li, Guangli Wang, Xueying Ma, Xiu Liu, Lei Feng, Xiaobing State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China College of Computer Science and Technology Jilin University Changchun China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun China

ISBN: (纸本)9783030495558

In this paper, we present XDN, an optimization and inference engine for accelerating residual neural networks on Cambricon chips. We leverage a channel pruning method to compress the weights of ResNet-50. By exploring the optimization opportunities in computational graphs, we propose a layer fusion strategy, which dramatically decreases the number of scalar computation layers, such as Batch Normalization, Scale. Furthermore, we design an efficient implementation of XDN, including data preprocessing, hyper-parameter auto-tuning, etc. The experimental results show that the ResNet-50 model can achieve significant speedup without accuracy loss by using our XDN engine. © Springer Nature Switzerland AG 2020.

关键词： Engines

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The Hitchhiker's Guide to Fused Twins: A Review of Access to Digital Twins in situ in Smart Cities

arXiv

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arXiv 2022年

作者： Grübel, Jascha Thrash, Tyler Aguilar, Leonel Gath-Morad, Michal Chatain, Julia Sumner, Robert W. Hölscher, Christoph Schinazi, Victor R. Cognitive Science ETH Zurich Switzerland Game Technology Center ETH Zurich Switzerland Visual Computing Group Harvard University United States Center for Sustainable Future Mobility ETH Zurich Switzerland Geoinformation Engineering ETH Zurich Switzerland Department of Biology Saint Louis University United States Data Science Systems and Services Group ETH Zurich Switzerland Department of Architecture University of Cambridge United Kingdom Bartlett School of Architecture University College London United Kingdom Professorship for Learning Sciences and Higher Education ETH Zurich Switzerland Department of Psychology Bond University Australia

Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (Physical, Data, Analytical, Virtual, and Connection environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information Systems, Building/City Information Models, and DTs and provide an overview of future directions. © 2022, CC BY.

关键词： Sensor networks

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A Novel Cross-layer Mobile Data-gathering Protocol for Underwater Sensor Networks

A Novel Cross-layer Mobile Data-gathering Protocol for Under...

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IEEE Conference on Vehicular Technology (VTC)

作者： Faisal Abdulaziz Alfouzan Seyed Mohammad Ghoreyshi Alireza Shahrabi Mahsa Sadeghi Ghahroudi College of Engineering and Architecture Al Yamamah University Riyadh Saudi Arabia School of Mathematical Sciences University of Southampton Southampton UK School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

In this paper, a novel Cross-Layer Mobile Data gathering (CLMD) scheme for Underwater Sensor Networks (UWSNs) is presented to improve the performance with a cross-layer design in the MAC and routing layer. CLMD develops a distributed cross-layer mobile data gathering solution that allows multiple sensors to efficiently and fairly share the bandwidth-limited high-delay underwater acoustic medium and aggregate data packets in a group of cluster heads which are visited by Autonomous Underwater Vehicle (AUV) in the periodic tours. The network lifetime is prolonged by reselecting the cluster heads in the different operational phases while the tour length is kept short to reduce the latency. The effectiveness of CLMD is validated through an extensive simulation study which reveals the performance improvement in the energy-saving, network lifetime, and packet delivery ratio.

关键词： Distributed databases Media Access Protocol Wireless sensor networks Bandwidth Sensor phenomena and characterization

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Interdisciplinary space lab: Undergraduate curriculum innovation utilising astronaut health and wellbeing requirements for long duration space missions (LDMs) 70

Interdisciplinary space lab: Undergraduate curriculum innova...

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70th International Astronautical Congress, IAC 2019

作者： Alexander, Sasha Bannova, Olga School of Computing Engineering and Mathematics Western Sydney University SydneyNSW Australia Cullen College of Engineering United States Sasakawa International Center for Space Architecture University of Houston HoustonTX United States

This paper presents innovative curriculum for an Australian undergraduate industrial design program "Interdisciplinary Space Lab" that focuses on discovery and experiential learning in planning for future complex work in dynamic practice environments through concept development, iterative prototyping processes, and collaborative multiple discipline viewpoints. Universities seek to introduce undergraduate students to the importance of entering interdisciplinary practice environments despite there being limited information on how to proceed. A long duration mission (LDM) project based learning 'design for space' studio focusing on astronaut health and wellbeing has been developed for students who have completed 1.5 years of university study in any disciplinary area. This envisioned practice environment seeks to expand diffusion of space studies and importantly stimulate motivational strategies that are inclusive across discipline boundaries toward the merits of multi-modal ways of seeing, developing and collaborating through an iterative design process from concept to prototype embracing design thinking in diverse projects. The interdisciplinary space studio's framework acknowledges discipline-based specialisations and the intersection potential of synergistic team approaches through heightened understanding of cross-disciplinary vocabularies. The action-learning lab captures positive general public sentiment toward space discoveries and educative insights of past space exploration and space education with the view that experiencing novel and impactful nature of a wider variety of disciplinary skills and in complementing one's own field can stimulate a continued interest in the promise of the future. Reaching out to disciplines beyond engineering professions including medicine, sports science, psychology, economics, neuroscience, ICT, management, health sciences, construction, education, industrial design, architecture, materials science, nursing, and cultural studies

关键词： Students

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Computational homogenization method for the bending analysis of submarine power cables

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Journal of Physics: Conference Series 2024年第2期2647卷 022010-022010页

作者： Pan Fang Xiao Li Xiaoli Jiang Hans Hopman Yong Bai Department of Maritime and Transport Technology Delft University of Technology Netherlands Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way #16-16 Connexis 138632 Singapore College of Civil Engineering and Architecture Zhejiang University Hangzhou Zhejiang P.R. China

The complex structure and material property of a cable, particularly the stick-slip issue among its components pose the challenge for the bending analysis of submarine power cables. The calculation time and convergence problem of a full model makes the simulation unpractical during the design phase. This paper takes advantage of the peculiar structural property of helical components inside a cable, proposing a computational homogenization approach for analyzing the cable behavior under bending from global and local perspectives. This method assumes a macro model that is based on the theory of periodic beamlike structure, and a short-size micro model that is solved through a detailed finite element study. Results demonstrate the efficiency and capability of the proposed model that considers the structure nonlinearity and contact condition of a multi-layer cable with helical wires.

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Motor adaptation and internal model formation in a robot-mediated forcefield

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Psychoradiology 2021年第2期1卷 73-87页

作者： Myriam Taga Annacarmen Curci Sara Pizzamigglio Irene Lacal Duncan L.Turner Cynthia H.Y.Fu 无 School of Health Sports and BioscienceUniversity of East LondonLondonUK Department of Computer Science School of ArchitectureComputing and EngineeringUniversity of East LondonLondonUK Department of Physiology and Pharmacology Sapienza UniversityRomeItaly School of Psychology University of East LondonLondonUK Centre for Affective Disorders Institute of PsychiatryPsychology and NeuroscienceKing’s College LondonLondonUK

Background:Motor adaptation relies on error-based learning for accurate movements in changing ***,the neurophysiological mechanisms driving individual differences in performance are *** magnetic stimulation(TMS)-evoked potential can provide a direct measure of cortical ***:To investigate cortical excitability as a predictor of motor learning and motor adaptation in a robot-mediated ***:A group of 15 right-handed healthy participants(mean age 23 years)performed a robot-mediated forcefield perturbation *** were two conditions:unperturbed non-adaptation and perturbed *** was applied in the resting state at baseline and following motor adaptation over the contralateral primary motor cortex(left M1).Electroencephalographic(EEG)activity was continuously recorded,and cortical excitability was measured by TMS-evoked potential(TEP).Motor learning was quantified by the motor learning ***:Larger error-related negativity(ERN)in fronto-central regions was associated with improved motor per-formance as measured by a reduction in trajectory *** TEP N100 peak amplitude predicted motor learning(P=0.005),which was significantly attenuated relative to baseline(P=0.0018)following motor ***:ERN reflected the formation of a predictive internal model adapted to the forcefield *** in TEP N100 amplitude reflected an increase in cortical excitability with motor adaptation reflecting neuroplastic changes in the sensorimotor *** N100 is a potential biomarker for predicting the outcome in robot-mediated therapy and a mechanism to investigate psychomotor abnormalities in depression.

关键词： EEG TMS motor adaptation robot-mediated forcefield N100 ERN

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Predicting the Governing Factors for the Release of Colloidal Phosphorus from Arable, Forest, and Desert Soils to Different Water Bodies Using Structural Equation and Machine Learning Models

SSRN

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SSRN 2023年

作者： Khan, Sangar Gao, Huimin Milham, Paul J. Eltohamy, Kamel Mohamed Ullah, Habib Mu, Hongli Gao, Meixiang Yang, Xiaodong Hamid, Yasir Hooda, Peter S. Shaheen, Sabry M. Wu, Naicheng Zhejiang University China Department of Geography and Spatial Information Techniques Ningbo University Ningbo315211 China Hawkesbury Institute for the Environment University of Western Sydney LB 1797 PenrithNSW2751 Australia Water Relations and Field Irrigation Department Agricultural and Biological Research Division National Research Centre Cairo12622 Egypt College of Environmental Resource Sciences Zhejiang University Hangzhou310058 China Innovation Center of Yangtze River Delta Zhejiang University Zhejiang311400 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

The use of machine learning and artificial intelligence models in predicting colloidal phosphorus (P) release on a global scale is a relatively new area of research. Therefore, in this study we investigated the major determinants of colloidal P release from different land use types (i.e., farmland, desert, and forest soils) to rivers. The study utilizes the structural equation model (SEM) and three learning models (Random Forest, Support Vector Regression and SHAP Xgboost) to predict the release of colloidal P by using soil iron (Fe), aluminum (Al), calcium (Ca), pH, and total organic carbon (TOC) as independent variables. Results show that colloidal cations (Fe, Al, Ca) and colloidal TOC strongly influence colloidal P release, with bioclimatic variables and pH having a weaker effect. The SHAP Xgboost model outperforms the other models, with an R2 value of 0.94 and an RSME of 0.09. The relative importance ranking of the variables for predicting colloidal P release is consistent across all three models, with colloidal TOC ranked as the most important factor followed by colloidal Ca, colloidal Al, and precipitation. Partial dependence plot analysis demonstrates that increasing colloidal TOC leads to increased colloidal P release. These findings provide valuable insights into the major determinants of colloidal P-release soils that can be used to develop effective management strategies to reduce P losses from different land use types to water bodies and thus mitigate the potential eutrophication impacts. © 2023, The Authors. All rights reserved.

关键词： Eutrophication

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Corrigendum to “An analysis of urban sprawl growth and prediction using remote sensing and machine learning techniques” [J. S. Am. Earth Sci. Volume 142 , 15 August 2024, 104988]

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Journal of South American Earth Sciences 2025年 152卷

作者： Alanoud Al Mazroa Mashael Maashi Fadoua Kouki Kamal M. Othman Nahla Salih Mohamed Ahmed Elfaki Sabarunisha Begum S Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Po box 103786 Riyadh 11543 Saudi Arabia Department of Financial and Banking Sciences Applied College at Muhail Aseer King Khalid University Saudi Arabia Department of Electrical Engineering College of Engineering and Islamic Architecture Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Applied College Imam Abdulrahman Bin Faisal University Saudi Arabia Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Department of Biotechnology P.S.R. Engineering College Sivakasi 626140 India

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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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