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作者： Erik Carl Bennett The Graduate Center City University of New York

学位级别：博士

The purpose of this study is to gain a deeper understanding of how faculty view cloud-based computing, how they perceive issues of privacy, security, and trust when using cloud-based systems in schools, and what differences, if any, exist between their at home use of cloud-based computer systems and their use of these and similar systems at work. Educators who took part in this study (a) demonstrated a relatively good understanding of the cloud; (b) perceived the issues of privacy, security, and trust as related to cloud-based computing as a serious matter, which strongly influenced their acceptance of the cloud, and to a lesser extent, their use of the cloud; and (c) had noticeable differences in their perceptions of the cloud when used for school related tasks, and then, as used for personal, non-work related tasks. The theoretical framework utilized is an adaption of F. D. Davis's 1989 Technology Acceptance Model, which according to Venkatesh (2000), is the most widely applied model of users' acceptance and usage. Findings from this study inform efforts to improve educators' understanding of the cloud as a dynamic technology with constantly evolving trade offs of convenience that are increasingly becoming the enemy of privacy, security, and trust.

关键词： K-12 Public Education cloud-based computing perceptions of Privacy Security Trust GAFE G-Suite

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cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging

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BIOMEDICAL PHYSICS & ENGINEERING EXPRESS 2024年第4期10卷 045053-045053页

作者： Bayerlein, Reimund Swarnakar, Vivek Selfridge, Aaron Spencer, Benjamin A. Nardo, Lorenzo Badawi, Ramsey D. Univ Calif Davis Dept Biomed Engn Davis CA 95616 USA Univ Calif Davis Dept Radiol Davis CA 95616 USA

Objective. This study investigates the potential of cloud-based serverless computing to accelerate Monte Carlo (MC) simulations for nuclear medicine imaging tasks. MC simulations can pose a high computational burden-even when executed on modern multi-core computing servers. cloud computing allows simulation tasks to be highly parallelized and considerably accelerated. Approach. We investigate the computational performance of a cloud-based serverless MC simulation of radioactive decays for positron emission tomography imaging using Amazon Web Service (AWS) Lambda serverless computing platform for the first time in scientific literature. We provide a comparison of the computational performance of AWS to a modern on-premises multi-thread reconstruction server by measuring the execution times of the processes using between 105 and 21010 simulated decays. We deployed two popular MC simulation frameworks-SimSET and GATE-within the AWS computing environment. Containerized application images were used as a basis for an AWS Lambda function, and local (non-cloud) scripts were used to orchestrate the deployment of simulations. The task was broken down into smaller parallel runs, and launched on concurrently running AWS Lambda instances, and the results were postprocessed and downloaded via the Simple Storage Service. Main results. Our implementation of cloud-based MC simulations with SimSET outperforms local server-based computations by more than an order of magnitude. However, the GATE implementation creates more and larger output file sizes and reveals that the internet connection speed can become the primary bottleneck for data transfers. Simulating 109 decays using SimSET is possible within 5 min and accrues computation costs of about $10 on AWS, whereas GATE would have to run in batches for more than 100 min at considerably higher costs. Significance. Adopting cloud-based serverless computing architecture in medical imaging research facilities can considerably

关键词： cloud-based computing Monte-Carlo simulation AWS lambda SimSET GATE nuclear medicine imaging computational efficiency

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Keeping an eye on moving objects: processing continuous spatial-keyword range queries

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GEOINFORMATICA 2024年第1期28卷 117-143页

作者： Orabi, Mariam Al Aghbari, Zaher Kamel, Ibrahim Mouheb, Djedjiga Univ Sharjah Coll Comp & Informat Univ City Rd Sharjah U Arab Emirates

With the emergence of GPS-equipped portable devices and Online Social Networks, geo-tagged textual data have been highly produced on a continuous basis, which can provide important information for various applications, such as marketing, disaster response, and so on. Therefore, processing continuous spatial-keyword queries over streaming data is a hot topic for the research community nowadays. However, applying such queries to moving objects is computationally expensive due to the frequent updates of objects' information that will continuously change the queries' answers. Few research works focus on processing spatial-keyword queries over moving objects, so this problem demands more exploration by research. This paper proposes Lagic;a cloud-based solution scheme to process continuous spatial-keyword range queries over moving objects. Lagic is the first model that provides an exact solution to the problem and minimizes the overhead on users' devices. A parallelized in-memory indexing structure is proposed to ensure the efficiency and scalability of Lagic. Short-term Safe Regions and a new approach for Buffer Regions are presented to reduce the number of required computations to update queries' answer sets in an incremental manner. Evaluations show that Lagic can reduce the total processing time to seven folds less than a baseline model. It also provides better computational scalability and efficiency. Furthermore, Lagic shows stability in continuous running time against variations of queries' and objects' attributes.

关键词： Moving objects Spatial-textual objects Continuous query Spatial-keyword query cloud-based computing In-memory index

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Long term analysis of international wetlands in Iran: Monitoring surface water area and water balance

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2024年 126卷

作者： Amindin, Atiyeh Siamian, Narges Dereimi, Zeynab Najafi Clague, John J. Blaschke, Thomas Pourghasemi, Hamid Reza Shiraz Univ Coll Agr Dept Nat Resources & Environm Engn Shiraz Iran Czech Univ Life Sci Prague Fac Environm Sci Prague Czech Republic Simon Fraser Univ Inst Quaternary Res Dept Earth Sci Burnaby BC V5A 1S6 Canada Univ Salzburg Dept Geoinformat Z GIS Salzburg Austria Shiraz Univ Coll Agr Dept Soil Sci Shiraz Iran

Wetlands have rich biological diversity and productivity, and support unique ecological functions. However, wetlands worldwide are under threat owing to the pressures of development and climate change. We analysed the extent and water balance of 21 important wetlands in Iran from 1990 to 2022 based on Landsat 5, 7, 8, and 9, and "Sentinel-2 satellite images" and Era5-land in the "Google Earth Engine (GEE)" platform. We performed morphometric analyses using NKU-MAN and the Mann-Kendall test to estimate the trends for wetland water bodies. Most seasonal water balance maxima occurred in January, followed by February and, in some cases, in the adjacent months, whilst the minima primarily occurred in May (nine wetlands) and June (five wetlands). Most importantly, the results revealed a gradual decrease in the size of the water bodies over the course of the study period, although this trend was not statistically significant for all water bodies. The findings of this study provide information that can be used by policymakers, planners, scientists, and residents to assess and manage sustainable water usage in wetland watersheds in a changing climate.

关键词： Climate driving forces cloud-based computing Era5-land Mann-Kendall NDWI SDG6 Water budget

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Modeling and Empirical Validation of Reliability and Performance Trade-Offs of Dynamic Routing in Service- and cloud-based Architectures

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IEEE TRANSACTIONS ON SERVICES computing 2022年第6期15卷 3372-3386页

作者： Amiri, Amirali Zdun, Uwe van Hoorn, Andre Univ Vienna Software Architecture Grp Fac Comp Sci A-1010 Vienna Austria Univ Vienna Software Architecture Fac Comp Sci A-1010 Vienna Austria Univ Stuttgart Inst Software Technol D-70174 Stuttgart Germany

Context: Various patterns of dynamic routing architectures are used in service- and cloud-based environments, including sidecar-based routing, routing through a central entity such as an event store, or architectures with multiple dynamic routers. Objective: Choosing the wrong architecture may severely impact the reliability or performance of a software system. This article's objective is to provide models and empirical evidence to precisely estimate the reliability and performance impacts. Method:We propose an analytical model of request loss for reliability modeling. We studied the accuracy of this model's predictions empirically and calculated the error rate in 200 experiment runs, during which we measured the round-trip time performance and created a performance model based on multiple regression analysis. Finally, we systematically analyzed the reliability and performance impacts and trade-offs. Results and Conclusions:The comparison of the empirical data to the reliability model's predictions shows a low enough and converging error rate for using the model during system architecting. The predictions of the performance model show that distributed approaches for dynamic data routing have a better performance compared to centralized solutions. Our results provide important new insights on dynamic routing architecture decisions to precisely estimate the trade-off between system reliability and performance.

关键词： cloud-based computing service-based applications dynamic routing architectures system reliability performance

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RNN-Assisted Feature-Extraction VMD for Load Classification in cloud computing Platform

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2022年第6期31卷 2250106-2250106页

作者： Ma, Yiming Wang, Haixin Yang, Junyou Chen, Zhe Yuan, Jingwei Okoye, Martin Onyeka Shenyang Univ Technol Sch Elect Engn Shenliao West Rd 111 Shenyang 110870 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark State Grid Liaoning Elect Power Res Inst Siping St 55 Shenyang 110006 Peoples R China

cloud computing can improve the calculation and data storage ability for the control center in the power system. A new framework of the cloud-based control center is proposed in this paper. This cloud computing system can collect the load data from smart meters of the grid and classify demand-side management (DSM) loads that meet the specific requirements. The selected loads belong to the off-peak period (from 21:00 to 07:00 next day) and can contribute to shifting the night peak load. A feature extraction combined with Variational Mode Decomposition (FE-VMD) of the loads which can be trained in recurrent neural network (RNN) is proposed in this paper. Using the feature value to replace the actual load data, input data can be significantly reduced which is suitable for a vast amount of load in the power system. A case study of real load data from 200,000 customers has been classified with this method, and the accuracy is compared with the other methods. From simulation with MATLAB, it can be seen that the FE-VMD combined with the RNN method provides the best result of 89.8% recognition accuracy among these methods.

关键词： cloud-based computing power system load classification variational mode decomposition feature extraction demand-side management

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Communication and Sensing for Two-Wheelers' Safety in an Autonomous Shuttle Scenario 21

Communication and Sensing for Two-Wheelers' Safety in an Aut...

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21st Mediterranean Communication and Computer Networking Conference (MedComNet)

作者： Perpetua, Joao Rito, Pedro Sargento, Susana Luis, Miguel Castro, Francisco Inst Telecomunicac P-3810193 Aveiro Portugal Univ Aveiro Dept Eletron Telecomunicac & Informat P-3810193 Aveiro Portugal Inst Politecn Lisboa ISEL Inst Super Engn Lisboa P-1959007 Lisbon Portugal Bosch Car Multimedia Portugal SA P-4705820 Braga Portugal

ISBN: (纸本)9798350338843

Vehicular Ad-hoc Networks (VANETs) allow the exchange of vehicle information between vehicles or between vehicles and the infrastructure. Together with sensor units installed in the vehicles themselves, or in road-side infrastructures within the concept of smart cities, they allow the development of applications targeting the safety of urban mobility. In this work we introduce two-wheelers (2wheelers), a Vulnerable Road User, and we research an approach for their safety in the roads. For this purpose, we first define a set of mobility scenarios representative of the interactions that may occur between 2wheelers and the other vehicles, together with the definition of the communication technologies available to each element. We propose a 2wheelers' safety multi-technology communication and sensing system designed to support mobile, edge or cloud-based operations, depending on the scenario in hands. Evaluation tests, performed in a real-world environment using an autonomous shuttle as the vehicle and a bicycle as a 2wheeler, have shown the operation of the safety platform in the different scenarios with different communication technologies, sensors and their fusion, and how the safety approach is able to improve the safety of 2wheelers in the road.

关键词： Vehicular ad hoc Networks Intelligent Transport Systems two-wheelers Edge-based computing cloud-based computing Road Safety Applications

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Characterizing fluvial geomorphological change using Google Earth Engine (GEE) to support sustainable flood management in the rural municipality of El Faid

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ARABIAN JOURNAL OF GEOSCIENCES 2022年第5期15卷 1-16页

作者： Ali, Azedou Khattabi, Abdellatif Lahssini, Said Natl Sch Forestry Engn Rabat 11000 Morocco

Flash floods are one of the most damaging natural hazards in Morocco. Located in Souss-Massa watershed, El Faid municipality is prone to flooding and land degradation. Thus, protecting human lives and properties requires an action plan of adaptation to climate change risks. In this context, this study is aimed at identifying the main overflowing risk areas through an analysis of the dynamics of the fluvial geomorphology of three main Wadis/Rivers crossing the municipality territory and providing an operational action plan for sustainable water and soil management. We used Google Earth Engine (GEE) as remote sensing cloud-based computing platform to detect the spatiotemporal evolution of the main Wadis using high-spatialresolution imagery (sentinel-2) at a small-scale instead of traditional desktop approaches. Results show that a significant increase of 64 ha, 36 ha, and 182 ha, respectively, affected area of Imaouen, Ait El Hssine, and Tadenasst Wadis from 2015 to 2020. Even more, CHIRPS Pentad climate datasets demonstrated a clear effect of the nature of the precipitation on the risk of overflowing of these watercourses. An action plan was proposed with three concrete intervention maps for each Wadi to improve flood control. The conservation measures suggested will help reduce flood risk and improve land management.

关键词： Land degradation Climate change Fluvial geomorphology Google Earth Engine cloud-based computing Morocco

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Using a cloud-based computing Environment to Support Teacher Training on Common Core Implementation

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TECHTRENDS 2013年第6期57卷 57-60页

作者： Robertson, Cory Anaheim City Sch Dist Anaheim CA 92805 USA

A cloud-based computing environment, Google Apps for Education (GAFE), has provided the Anaheim City School District (ACSD) a comprehensive and collaborative avenue for creating, sharing, and editing documents, calendars, and social networking communities. With this environment, teachers and district staff at ACSD are able to utilize the deep feature-set to assist with the implementation of the Common Core State Standards and the new shift in research-based lesson design. Through the use of the cloud-based computing environment, teachers in the district are able to receive support for the effective implementation of the Common Core State Standards.

关键词： cloud-based computing Google Apps for Education Common Core State Standards Elementary School Teacher Support

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Identifying and training deep learning neural networks on biomedical-related datasets

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BRIEFINGS IN BIOINFORMATICS 2024年第Sup1期25卷 bbae232-bbae232页

作者： Woessner, Alan E. Anjum, Usman Salman, Hadi Lear, Jacob Turner, Jeffrey T. Campbell, Ross Beaudry, Laura Zhan, Justin Cornett, Lawrence E. Gauch, Susan Quinn, Kyle P. Univ Arkansas Arkansas Integrat Metab Res Ctr Fayetteville AR USA Univ Arkansas Dept Biomed Engn 123 John A White Jr Engn Hall Fayetteville AR 72701 USA Univ Cincinnati Dept Comp Sci Cincinnati OH USA Univ Arkansas Dept Comp Sci & Comp Engn Fayetteville AR USA Deloitte Consulting LLP Hlth Data & AI Arlington VA USA Google Cloud Reston VA USA Univ Arkansas Med Sci Dept Physiol & Cell Biol Little Rock AR USA

This manuscript describes the development of a resources module that is part of a learning platform named 'NIGMS Sandbox for cloud-based Learning' https://***/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and breadth of these datasets increases. Artificial intelligence, and specifically deep learning neural networks, have recently become an important tool in novel biomedical research. However, use is limited due to their computational requirements and confusion regarding different neural network architectures. The goal of this learning module is to introduce types of deep learning neural networks and cover practices that are commonly used in biomedical research. This module is subdivided into four submodules that cover classification, augmentation, segmentation and regression. Each complementary submodule was written on the Google cloud Platform and contains detailed code and explanations, as well as quizzes and challenges to facilitate user training. Overall, the goal of this learning module is to enable users to identify and integrate the correct type of neural network with their data while highlighting the ease-of-use of cloud computing for implementing neural *** manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for cloud-based Learning'' https://***/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [] at the beginning of this Supplement

关键词： cloud-based computing deep learning artificial intelligence biomedical research engineering education

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