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Crowdsourcing : cloud-based software development
\"This book presents the latest research on the software crowdsourcing approach to develop large and complex software in a cloud-based platform. It develops the fundamental principles, management organization and processes, and a cloud-based infrastructure to support this new software development approach. The book examines a variety of issues in software crowdsourcing processes, including software quality, costs, diversity of solutions, and the competitive nature of crowdsourcing processes. Furthermore, the book outlines a research roadmap of this emerging field, including all the key technology and management issues for the foreseeable future. Crowdsourcing, as demonstrated by Wikipedia and Facebook for online web applications, has shown promising results for a variety of applications, including healthcare, business, gold mining exploration, education, and software development. Software crowdsourcing is emerging as a promising solution to designing, developing and maintaining software. Preliminary software crowdsourcing practices and platforms, including Apple's App Store and TopCoder, demonstrate the advantages of crowdsourcing in terms of software ecosystem expansion and product quality improvement.\"--Back cover.
Book
Dynamic quality management for cloud labor services : methods and applications for gaining reliable work results with an on-demand workforce
How can a scalable and efficient quality management mechanism for cloud labor services be designed in a way that it delivers results with a well-defined level of quality to the requester? Cloud labor services are a specific form of crowdsourcing: A coordination platform serves as an interface between requesters who need to get work done and a large crowd of workers who want to perform work. An early example of such a platform is Amazon's Web marketplace Mturk, on which service requesters can publish open calls for so-called human intelligence tasks (HITs). Robert Kern's work makes a considerable contribution toward solving the quality problem for scalable human work. On the basis of a comprehensive framework of cloud labor, he develops a set of methods to conceptually measure and aggregate the quality of human work results, implements a platform to put those methods to work, and evaluates their application in a number of very compelling, real-world scenarios successfully combining concepts from statistics, information technology, and management. Reading this book will be beneficial to novices in cloud labor services looking for orientation in this new field as well as to advanced researchers and practitioners developing cloud quality concepts.--Back cover.
Book
Internet of Things (IoT) Based Design of a Secure and Lightweight Body Area Network (BAN) Healthcare System
As sensor networks and cloud computation technologies have rapidly developed over recent years, many services and applications integrating these technologies into daily life have come together as an Internet of Things (IoT). At the same time, aging populations have increased the need for expanded and more efficient elderly care services. Fortunately, elderly people can now wear sensing devices which relay data to a personal wireless device, forming a body area network (BAN). These personal wireless devices collect and integrate patients’ personal physiological data, and then transmit the data to the backend of the network for related diagnostics. However, a great deal of the information transmitted by such systems is sensitive data, and must therefore be subject to stringent security protocols. Protecting this data from unauthorized access is thus an important issue in IoT-related research. In regard to a cloud healthcare environment, scholars have proposed a secure mechanism to protect sensitive patient information. Their schemes provide a general architecture; however, these previous schemes still have some vulnerability, and thus cannot guarantee complete security. This paper proposes a secure and lightweight body-sensor network based on the Internet of Things for cloud healthcare environments, in order to address the vulnerabilities discovered in previous schemes. The proposed authentication mechanism is applied to a medical reader to provide a more comprehensive architecture while also providing mutual authentication, and guaranteeing data integrity, user untraceability, and forward and backward secrecy, in addition to being resistant to replay attack.
Journal Article
Monitoring periodically national land use changes and analyzing their spatiotemporal patterns in China during 2015–2020
High-resolution mapping and monitoring of national land use/cover changes contribute significantly to the knowledge of the interaction between human activities and environmental changes. China’s Land Use/cover Dataset (CLUD) for 2020 and its dynamic changes in 2015–2020 were developed to extend the CLUD to over 30 years (i.e., the 1980s to 2020 at 5-year intervals) by integrating remote sensing big data and knowledge-based human-computer interaction interpretation methods. This integrating method for CLUD 2020 improved the efficiency of national land use/cover mapping and the accuracy of land use pattern change detection compared to earlier CLUD products, with an overall accuracy of 95%. The intensity of land use change decreased across China in 2015–2020 compared to 2010–2015, although both characteristics of its spatial changes were similar. The cropland area continued to shrink at national scale in 2015–2020, with two regional hotspots including the widespread conversions from dry land into paddy land in Northeast China and the coexistence of widespread land cultivation and cropland abandonment in Xinjiang of Northwest China. Built-up land area continued to expand in China, showing consistency between 2015–2020 and 2010–2015, in which hotspots transited from the surroundings of coastal megacities to the city surroundings of the central and western zones. For natural land, although the woodland and grassland decreased in 2015–2020, its magnitude expanded compared to 2010–2015. In comparison, the water body area in Qinghai-Tibet Plateau increased significantly under the continuous impact of climate change. These characteristics of land use change were closely related to the development strategy of the top-level design of the 13th Five-Year Plan (2016–2020) (e.g., ecological civilization construction and high-quality development).
Journal Article
MONITORING COASTAL AREAS USING NDWI FROM LANDSAT IMAGE DATA FROM 1985 BASED ON CLOUD COMPUTATION GOOGLE EARTH ENGINE AND APPS
The coastal area is an area that has a dense population with a lot of human activities that occur there. Due to environmental changes and human activities, changes often occur in coastal areas ranging from erosion and sedimentation. Changes must continuously be monitored to plan countermeasures due to the occurring phenomena. This study aims to create a website-based application to monitor coastal areas. This study will use Landsat data 5,7,8, and 9 to see changes in coastal areas. The analysis can be provided from 1985 until recent data by integrating four Landsat satellites. The NDWI index (Normalized Difference Wetness Index) analyzes changes occurring in coastal areas and differentiates between water and land area. The analysis is not only in the form of changes that occur in coastal areas but also in time series analysis, and trends that occur at a point can be analyzed using land trend analysis. The resulting website based on Cloud Computation in Google Earth Engine can be seen at the link https://bit.ly/MonitoringPesisir. This website can automatically update, and users can choose the location to monitor. This research is expected to be used by policymakers to monitor and plan the development and regulation of coastal areas.
Journal Article
Developing a Reproducible Microbiome Data Analysis Pipeline Using the Amazon Web Services Cloud for a Cancer Research Group: Proof-of-Concept Study
Cloud computing for microbiome data sets can significantly increase working efficiencies and expedite the translation of research findings into clinical practice. The Amazon Web Services (AWS) cloud provides an invaluable option for microbiome data storage, computation, and analysis.
The goals of this study were to develop a microbiome data analysis pipeline by using AWS cloud and to conduct a proof-of-concept test for microbiome data storage, processing, and analysis.
A multidisciplinary team was formed to develop and test a reproducible microbiome data analysis pipeline with multiple AWS cloud services that could be used for storage, computation, and data analysis. The microbiome data analysis pipeline developed in AWS was tested by using two data sets: 19 vaginal microbiome samples and 50 gut microbiome samples.
Using AWS features, we developed a microbiome data analysis pipeline that included Amazon Simple Storage Service for microbiome sequence storage, Linux Elastic Compute Cloud (EC2) instances (ie, servers) for data computation and analysis, and security keys to create and manage the use of encryption for the pipeline. Bioinformatics and statistical tools (ie, Quantitative Insights Into Microbial Ecology 2 and RStudio) were installed within the Linux EC2 instances to run microbiome statistical analysis. The microbiome data analysis pipeline was performed through command-line interfaces within the Linux operating system or in the Mac operating system. Using this new pipeline, we were able to successfully process and analyze 50 gut microbiome samples within 4 hours at a very low cost (a c4.4xlarge EC2 instance costs $0.80 per hour). Gut microbiome findings regarding diversity, taxonomy, and abundance analyses were easily shared within our research team.
Building a microbiome data analysis pipeline with AWS cloud is feasible. This pipeline is highly reliable, computationally powerful, and cost effective. Our AWS-based microbiome analysis pipeline provides an efficient tool to conduct microbiome data analysis.
Journal Article
GBC: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species
Whole -genome sequencing projects of millions of subjects contain enormous genotypes, entailing a huge memory burden and time for computation. Here, we present GBC, a toolkit for rapidly compressing large-scale genotypes into highly addressable byte-encoding blocks under an optimized parallel framework. We demonstrate that GBC is up to 1000 times faster than state-of-the-art methods to access and manage compressed large-scale genotypes while maintaining a competitive compression ratio. We also showed that conventional analysis would be substantially sped up if built on GBC to access genotypes of a large population. GBC’s data structure and algorithms are valuable for accelerating large-scale genomic research.
Journal Article
An Intelligent Rice Yield Trait Evaluation System Based on Threshed Panicle Compensation
High-throughput phenotyping of yield-related traits is meaningful and necessary for rice breeding and genetic study. The conventional method for rice yield-related trait evaluation faces the problems of rice threshing difficulties, measurement process complexity, and low efficiency. To solve these problems, a novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy. To improve the threshed panicle detection accuracy, the Region of Interest Align, Convolution Batch normalization activation with Leaky Relu module, Squeeze-and-Excitation unit, and optimal anchor size have been adopted to optimize the Faster-RCNN architecture, termed ‘TPanicle-RCNN,’ and the new model achieved F1 score 0.929 with an increase of 0.044, which was robust to indica and japonica varieties. Additionally, AI cloud computing was adopted, which dramatically reduced the system cost and improved flexibility. To evaluate the system accuracy and efficiency, 504 panicle samples were tested, and the total spikelet measurement error decreased from 11.44 to 2.99% with threshed panicle compensation. The average measuring efficiency was approximately 40 s per sample, which was approximately twenty times more efficient than manual measurement. In this study, an automatic and intelligent system for rice yield-related trait evaluation was developed, which would provide an efficient and reliable tool for rice breeding and genetic research.
Journal Article
Mapping the Dynamics of Winter Wheat in the North China Plain from Dense Landsat Time Series (1999 to 2019)
Monitoring spatio-temporal changes in winter wheat planting areas is of high importance for the evaluation of food security. This is particularly the case in China, having the world’s largest population and experiencing rapid urban expansion, concurrently, it puts high pressure on food demands and the availability of arable land. The relatively high spatial resolution of Landsat is required to resolve the historical mapping of smallholder wheat fields in China. However, accurate Landsat-based mapping of winter wheat planting dynamics over recent decades have not been conducted for China, or anywhere else globally. Based on all available Landsat TM/ETM+/OLI images (~28,826 tiles) using Google Earth Engine (GEE) cloud computing and a Random Forest machine-learning classifier, we analyzed spatio-temporal dynamics in winter wheat planting areas during 1999–2019 in the North China Plain (NCP). We applied a median value of 30-day sliding windows to fill in potential data gaps in the available Landsat images, and six EVI-based phenological features were then extracted to discriminate winter wheat from other land cover types. Reference data for training and validation were extracted from high-resolution imagery available via Google Earth™ online mapping service, Sentinel-2 and Landsat imagery. We ran a sensitivity analysis to derive the optimal training sample class ratio (β = 1.8) accounting for the unbalanced distribution of land-cover types. We mapped winter wheat planting areas for 1999–2019 with overall accuracies ranging from 82% to 99% and the user’s/producer’s accuracies of winter wheat range between 90% and 99%. We observed an overall increase in winter wheat planting areas of 1.42 × 106 ha in the NCP as compared to the year 2000, with a significant increase in the Shandong and Hebei provinces (p < 0.05). This result contrasts the general discourse suggesting a decline in croplands (e.g., rapid urbanization) and climate change-induced unfavorable cropping conditions in the NCP. This suggests adjustments of the winter wheat planting area over time to satisfy wheat supply in relation to food security. This study highlights the application of Landsat images through GEE in documenting spatio-temporal dynamics of winter wheat planting areas for adequate management of cropping systems and assessing food security in China.
Journal Article