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Governments across the world are starting to make a dynamic shift to cloud computing so as to increase efficiency. Although, the cloud technology brings various benefits for government organisations, including flexibility and low cost, adopting it with the existing system is not an easy task. In this regard, the most significant challenge to any government agency is security concern. Our previous study focused to identify security factors that influence decision of government organisations to adopt cloud. This research enhances the previous work by investigating on the impact of various independent security related factors on the adopted security taxonomy based on critical ratio, standard error and significance levels. Data was collected from IT and security experts in the government organisations of Saudi Arabia. The Analysis of Moment Structures (AMOS) tool was used in this research for data analysis. Critical ratio reveals the importance of Security Benefits, Risks and Awareness Taxonomies on cloud adoption. Also, most of the exogenous variables had strong and positive relationships with their fellow exogenous variables. In future, this taxonomy model can also be applied for studying the adoption of new IT innovations whose IT architecture is similar to that of the cloud. ; N/A

The Sentinel-2 satellite mission, developed by the European Space Agency (ESA) for the Copernicus program of the European Union, provides repetitive multi-spectral observations of all Earth land surfaces at a high resolution. The Level 2A product is a basic product requested by many Sentinel-2 users: it provides surface reflectance after atmospheric correction, with a cloud and cloud shadow mask. The cloud/shadow mask is a key element to enable an automatic processing of Sentinel-2 data, and therefore, its performances must be accurately validated. To validate the Sentinel-2 operational Level 2A cloud mask, a software program named Active Learning Cloud Detection (ALCD) was developed, to produce reference cloud masks. Active learning methods allow reducing the number of necessary training samples by iteratively selecting them where the confidence of the classifier is low in the previous iterations. The ALCD method was designed to minimize human operator time thanks to a manually-supervised active learning method. The trained classifier uses a combination of spectral and multi-temporal information as input features and produces fully-classified images. The ALCD method was validated using visual criteria, consistency checks, and compared to another manually-generated cloud masks, with an overall accuracy above 98%. ALCD was used to create 32 reference cloud masks, on 10 different sites, with different seasons and cloud cover types. These masks were used to validate the cloud and shadow masks produced by three Sentinel-2 Level 2A processors: MAJA, used by the French Space Agency (CNES) to deliver Level 2A products, Sen2Cor, used by the European Space Agency (ESA), and FMask, used by the United States Geological Survey (USGS). The results show that MAJA and FMask perform similarly, with an overall accuracy around 90% (91% for MAJA, 90% for FMask), while Sen2Cor&rsquo ; s overall accuracy is 84%. The reference cloud masks, as well as the ALCD software used to generate them are made available to the Sentinel-2 user community.

The Sentinel-2 satellite mission, developed by the European Space Agency (ESA) for the Copernicus program of the European Union, provides repetitive multi-spectral observations of all Earth land surfaces at a high resolution. The Level 2A product is a basic product requested by many Sentinel-2 users: it provides surface reflectance after atmospheric correction, with a cloud and cloud shadow mask. The cloud/shadow mask is a key element to enable an automatic processing of Sentinel-2 data, and therefore, its performances must be accurately validated. To validate the Sentinel-2 operational Level 2A cloud mask, a software program named Active Learning Cloud Detection (ALCD) was developed, to produce reference cloud masks. Active learning methods allow reducing the number of necessary training samples by iteratively selecting them where the confidence of the classifier is low in the previous iterations. The ALCD method was designed to minimize human operator time thanks to a manually-supervised active learning method. The trained classifier uses a combination of spectral and multi-temporal information as input features and produces fully-classified images. The ALCD method was validated using visual criteria, consistency checks, and compared to another manually-generated cloud masks, with an overall accuracy above 98%. ALCD was used to create 32 reference cloud masks, on 10 different sites, with different seasons and cloud cover types. These masks were used to validate the cloud and shadow masks produced by three Sentinel-2 Level 2A processors: MAJA, used by the French Space Agency (CNES) to deliver Level 2A products, Sen2Cor, used by the European Space Agency (ESA), and FMask, used by the United States Geological Survey (USGS). The results show that MAJA and FMask perform similarly, with an overall accuracy around 90% (91% for MAJA, 90% for FMask), while Sen2Cor's overall accuracy is 84%. The reference cloud masks, as well as the ALCD software used to generate them are made available to the Sentinel-2 user community.

International audience ; The Sentinel-2 satellite mission, developed by the European Space Agency (ESA) for the Copernicus program of the European Union, provides repetitive multi-spectral observations of all Earth land surfaces at a high resolution. The Level 2A product is a basic product requested by many Sentinel-2 users: it provides surface reflectance after atmospheric correction, with a cloud and cloud shadow mask. The cloud/shadow mask is a key element to enable an automatic processing of Sentinel-2 data, and therefore, its performances must be accurately validated. To validate the Sentinel-2 operational Level 2A cloud mask, a software program named Active Learning Cloud Detection (ALCD) was developed, to produce reference cloud masks. Active learning methods allow reducing the number of necessary training samples by iteratively selecting them where the confidence of the classifier is low in the previous iterations. The ALCD method was designed to minimize human operator time thanks to a manually-supervised active learning method. The trained classifier uses a combination of spectral and multi-temporal information as input features and produces fully-classified images. The ALCD method was validated using visual criteria, consistency checks, and compared to another manually-generated cloud masks, with an overall accuracy above 98%. ALCD was used to create 32 reference cloud masks, on 10 different sites, with different seasons and cloud cover types. These masks were used to validate the cloud and shadow masks produced by three Sentinel-2 Level 2A processors: MAJA, used by the French Space Agency (CNES) to deliver Level 2A products, Sen2Cor, used by the European Space Agency (ESA), and FMask, used by the United States Geological Survey (USGS). The results show that MAJA and FMask perform similarly, with an overall accuracy around 90% (91% for MAJA, 90% for FMask), while Sen2Cor's overall accuracy is 84%. The reference cloud masks, as well as the ALCD software used to generate them are made available to the ...

Although military studies of the last ten years have shown that visible polarimetry supplies supplemental surveillance information, the polarimetric signatures of ground-based objects greatly depend on the illuminating skylight polarization. The polarization of a pure molecular atmosphere is easily modeled, but aerosols and clouds modify clear-sky polarization substantially. The Air Force has developed a polarimetric atmospheric radiative transfer model (MODTRAN-P) to simulate atmospheric effects. To assist MODTRAN-P code validation, a full-sky visible polarimeter has been developed using liquid crystal variable retarders (LCVRs). Unique calibration issues of LCVR instruments are addressed. A fisheye lens can be exchanged for a telephoto lens to provide system flexibility. This allows comparison between changing sky and changing target signatures. ; Calibration accuracy is within ±3% Degree of Linear Polarization (DoLP). Comparison of measured data with MODTRAN-P calculations shows that single-scatter models over-predict the sky polarization, while the improperly implemented multiple-scatter models under-predict it. Furthermore, a model comprising only one scattering path looking directly at a cloud insufficiently predicts polarization. The polarization is dependent upon whether or not there are clouds in surrounding areas. Similarly, clouds affect adjacent clear sky polarization, but further instrumentation is needed to understand whether this is caused by sub-visual cloud layers in these clear-sky areas or by illumination from neighboring clouds. Halo and cloud polarizations are also treated briefly.

Virtualization technology for cloud based services / Urmila Shrawankar, Chetan Dhule -- Hybrid cloud architecture for better cloud interoperability / Shweta Barhate, Mahendra Dhore -- Autoscaling techniques for web applications in the cloud / Priya Deokar, Sandhya Arora -- Community cloud service model for people with special needs / Sampada Wazalwar, Urmila Shrawankar -- Sensor applications in agriculture--a review / Jyoti Lagad, Sandhya Arora -- Crop biophysical parameters estimation using SAR imagery for precision agriculture applications / Vaishali G. Bhujade -- Importance of cloud computing technique in agriculture field using different methodologies / Arvind Shivappa Kapse, Avinash Shivappa Kapse, Vilas M. Thakare -- Optimal clustering scheme for cloud operations management over mobile ad hoc network of crop systems / Poonam A. Gaikwad, Swati S. Sherekar, Vilas M. Thakare -- A novel hybrid method for cloud security using efficient IDS for agricultural weather forecasting systems / Rakshanda Kishor Borikar, Swati S. Sherekar, Vilas M. Tharkare -- Cloud model for real time healthcare services / Urmila Shrawanker, Girish Talmale -- Cloud computing based smart healthcare system / Sumedha Sandip Borde, Varsha Ratnaparkhe -- Rehearsal of cloud and IoT device in health care system / Bhargavi Salil Chinchmalatpure -- Cloud based diagnostic and management framework for remote health monitoring / Bharati Dixit, Advait Brahme, Shaunak Choudhary, Manasi Agrawal, Atharva Viraj Kukade -- Efficient accessibility in cloud databases of health network with natural neighbor approach for RNN-DBSCAN / Rupali Wadnare, S.S.Sherekar, V.M. Thakare -- Blood oxygen level and pulse rate monitoring using IoT and cloud based data storage / -- Latesh Malik, Ameya Shahu, Sohan Rathod, Pranay Kuthe, Prachi Patil -- Parkinson disease prediction model and deployment on AWS cloud / Harshvardhan Tiwari, Shiji K. Shridhar, Preeti V. Patil, Sinchana K.R., Aishwarya G. -- Federated learning for brain tumor segmentation on cloud / Deep Gandhi, Jash Mehta, Nemil Shah, Ramchandra Mangrulkar -- Smart system for COVID-19 susceptibility test and prediction of risk along with validation of guidelines conformity using cloud / Rashmi Welekar, Manjiri Vairagade, Mohit Sawal, Shreya Rathi, Shrijeet Shivdekar, Siddhi Belgamwar -- Designing of policy data prediction framework in cloud for trending COVID-19 issues over social media / Shubham Nandkishor Ugale, Swati S. Sherekar, Vilas M. Thakare.

This document gathers the most important results related to the implementation, test and validation of each component and service in the Radiatus project. This project is focused on developing a Big Data Analytics service on a PaaS (platform as a service) architecture. For doing so, the document details each of the component's and service's implementation steps and how they can be combined in order to create BigData Analytics services. Moreover, it includes flow diagram of each component's execution and the communication's channels interconnection used to link components with the aim of creating a new service. Finally, this document shows all the test performed in order to validate the functionality of each service and it also illustrates the main validations done. This validation has been done using kernels which allows us to evaluate the performance of each component over the ECloud platform. ; Radiatus. Project funded by the Valencian Institute of Business Competitiveness (IVACE) and European Union through the European Regional Development Fund (ERDF), within the public grant program adressed to Technological Institutes of the Valencian Community for 2017 with 170.771,32€. File number: IMDEEA/2017/139

This work proposes a Cooperation-Competitive (Coopetitive) approach that allows coordinated work among the Secretary of Public Education (SEP), the Autonomous University of Querétaro (UAQ) and government funds from National Council for Science and Technology (CONACYT) or some other international organizations. To work on an overall knowledge transfer strategy with e-learning over the Cloud, where experts in junior high and high school education, working in multidisciplinary teams, perform analysis, evaluation, design, production, validation and knowledge transfer at large scale using a Cloud Computing platform. Allowing teachers and students to have all the information required to ensure a homologated nationally knowledge of topics such as mathematics, statistics, chemistry, history, ethics, civism, etc. This work will start with a pilot test in Spanish and initially in two regional dialects Otomí and Náhuatl. Otomí has more than 285,000 speaking indigenes in Queretaro and Mexico´s central region. Náhuatl is number one indigenous dialect spoken in Mexico with more than 1,550,000 indigenes. The phase one of the project takes into account negotiations with indigenous tribes from different regions, and the Information and Communication technologies to deliver the knowledge to the indigenous schools in their native dialect. The methodology includes the following main milestones: Identification of the indigenous areas where Otomí and Náhuatl are the spoken dialects, research with the SEP the location of actual indigenous schools, analysis and inventory or current schools conditions, negotiation with tribe chiefs, analysis of the technological communication requirements to reach the indigenous communities, identification and inventory of local teachers technology knowledge, selection of a pilot topic, analysis of actual student competence with traditional education system, identification of local translators, design of the e-learning platform, design of the multimedia resources and storage strategy for "Cloud ...

This document gathers the most important results related to the implementation, test and validation of the management system of Big Data Analytic Services. This project is focused on developing a cloud service that acts as an entry point to the Big Data Analytics services. For doing so, the document details each of the component's and service's implementation steps and how they can be combined in order to create BigData Analytics services. Moreover, it includes flow diagram of each component's execution and the communication's channels interconnection used to link components with the aim of creating a new service. Finally, this document shows all the test performed in order to validate the functionality of each service and it also illustrates the main validations done. This validation has been done using kernels which allows us to evaluate the performance of each component over the ECloud platform. ; Radiatus. Project funded by the Valencian Institute of Business Competitiveness (IVACE) and European Union through the European Regional Development Fund (ERDF), within the public grant program adressed to Technological Institutes of the Valencian Community for 2017 with 170.771,32€. File number: IMDEEA/2017/139

Cloud computing has emerged as a cutting-edge technology which is widely used by both private and public institutions, since it eliminates the capital expense of buying, maintaining, and setting up both hardware and software. Clients pay for the services they use, under the so-called Service Level Agreements (SLAs), which are the contracts that establish the terms and costs of the services. In this paper, we propose the CloudCost UML profile, which allows the modeling of cloud architectures and the users' behavior when they interact with the cloud to request resources. We then investigate how to increase the profits of cloud infrastructures by using price schemes. For this purpose, we distinguish between two types of users in the SLAs: regular and high-priority users. Regular users do not require a continuous service, so they can wait to be attended to. In contrast, high-priority users require a constant and immediate service, so they pay a greater price for their services. In addition, a computer-aided design tool, called MSCC (Modeling SLAs Cost Cloud), has been implemented to support the CloudCost profile, which enables the creation of specific cloud scenarios, as well as their edition and validation. Finally, we present a complete case study to illustrate the applicability of the CloudCost profile, thus making it possible to draw conclusions about how to increase the profits of the cloud infrastructures studied by adjusting the different cloud parameters and the resource configuration. ; This work was supported by the Spanish Ministry of Science and Innovation (co-financed by European Union FEDER funds) project "FAME (Formal modeling and advanced testing methods. Applications to medicine and computing systems) and MASSIVE (Engineering adaptive software by and for the people in a highly connected world)", references RTI2018-093608-B-C32 and RTI2018-095255-B-I00. There was also support from the Junta de Comunidades de Castilla-La Mancha project SBPLY/17/180501/000276/ 01 (cofunded with FEDER funds, EU), the Region of Madrid (grant number FORTE-CM, S2018/TCS-4314), and the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with the Complutense University as part of the Program to Stimulate Research for Young Doctors in the context of the V PRICIT (Regional Programme of Research and Technological Innovation) under grant PR65/19-22452

[EN] In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models. ; This work was supported by the project DEEP-Hybrid-DataCloud ``Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud'' that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant 777435 ; Lopez Garcia, A.; Marco De Lucas, J.; Antonacci, M.; Zu Castell, W.; David, M.; Hardt, M.; Lloret Iglesias, L. (2020). A Cloud-Based Framework for Machine Learning Workloads and Applications. IEEE Access. 8:18681-18692. https://doi.org/10.1109/ACCESS.2020.2964386 ; S ; 18681 ; 18692 ; 8

Abstract. In this paper we present a performance analysis of the European lightning location system EUCLID for cloud-to ground flashes/strokes in terms of location accuracy (LA), detection efficiency (DE) and peak current estimation. The performance analysis is based on ground truth data from direct lightning current measurements at the Gaisberg Tower (GBT) and data from E-field and video recordings. The E-field and video recordings were collected in three different regions in Europe, namely in Austria, Belgium and France. The analysis shows a significant improvement of the LA of the EUCLID network over the past 7 years. Currently, the median LA is in the range of 100 m in the center of the network and better than 500 m within the majority of the network. The observed DE in Austria and Belgium is similar, yet a slightly lower DE is determined in a particular region in France, due to malfunctioning of a relevant lightning location sensor during the time of observation. The overall accuracy of the lightning location system (LLS) peak current estimation for subsequent strokes is reasonable keeping in mind that the LLS-estimated peak currents are determined from the radiated electromagnetic fields, assuming a constant return stroke speed. The results presented in this paper can be used to estimate the performance of the EUCLID network related to cloud-to-ground flashes/strokes for regions with similar sensor baselines and sensor technology.

PurposeProduct Lifecycle Management (PLM) systems have gained wide popularity for their role in manufacturing organizations for creating, managing and distributing product data. These systems are one of various enterprise systems which are required for smooth functioning and meeting the scaling up requirements organization. However, with introduction of cloud technology and other industry 4.0 initiatives, there has been focus on moving the on-premises IT application to the cloud. Such a move needs to be carried out by identifying and evaluating various challenges. This paper aims to discuss the aforementioned objective.Design/methodology/approachThe challenges identified through literature review have also been confirmed to be present via interview, system observation and documentation review through case study-based validation in an automotive component manufacturing industry.FindingsThe article identifies needs and challenges of cloud PLM systems and presents a generic framework for developing an approach for cloud PLM journey for an organization. The article also provides an approach for resolving the different challenges to realizing the designed system.Originality/valueThe simplified generic framework has been presented for use by industry professionals and researchers for designing cloud PLM systems that would fulfill expectations of different levels of stakeholders.