2017 IEEE 13th International Conference on e-Science (e-Science)最新文献

{"title":"Spartan and NEMO: Two HPC-Cloud Hybrid Implementations","authors":"Lev Lafayette, B. Wiebelt","doi":"10.1109/eScience.2017.70","DOIUrl":"https://doi.org/10.1109/eScience.2017.70","url":null,"abstract":"High Performance Computing systems offer excellent metrics for speed and efficiency when using bare metal hardware, a high speed interconnect, and parallel applications. In contrast cloud computing has provided management and implementation flexibility at a cost of performance. We therefore suggest two approaches to make HPC resources available in a dynamically reconfigurable hybrid HPC/Cloud architecture. Both can can be achieved with few modifications to existing HPC/Cloud environments. The first approach, from the University of Melbourne, generates a consistent compute node operating system image with variation in the virtual hardware specification. The second approach, from the University of Freiburg, deploys a cloud-client on the HPC compute nodes, so the HPC hardware can run Cloud-Workloads for backfilling free compute slots.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134164449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Framework for Computing Artistic Style Using Artistically Relevant Features","authors":"C. Buell, W. Seeley, Ricky J. Sethi","doi":"10.1109/eScience.2017.57","DOIUrl":"https://doi.org/10.1109/eScience.2017.57","url":null,"abstract":"We present two artistically-relevant algorithms to aid in the quantification of artistic style, the Discrete Tonal Measure (DTM) and Discrete Variational Measure (DVM). These quantitative features can provide clues to the artistic elements that enable art scholars to categorize works as belonging to different artistic styles. We also introduce two new datasets for analysis of artistic style: one based on the school of art to which artists belong and one based on the medium used by a specific artist. We show results of initial experiments for classifying paintings on each of these datasets with DTM and DVM using a scientific workflows framework that will allow reuse and extension of many visual stylometry methods, as well as allowing easy reproducibility of analytical results, by publishing datasets and workflows packaged as linked data.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131939568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Platform for the Analysis of Qualitative and Quantitative Data about the Built Environment and Its Users","authors":"Mike Simpson, S. Woodman, H. Hiden, Sebastian Stein, Stephen Dowsland, Mark Turner, Vicki L. Hanson, P. Watson","doi":"10.1109/eScience.2017.36","DOIUrl":"https://doi.org/10.1109/eScience.2017.36","url":null,"abstract":"There are many scenarios in which it is necessary to collect data from multiple sources in order to evaluate a system, including the collection of both quantitative data - from sensors and smart devices - and qualitative data - such as observations and interview results. However, there are currently very few systems that enable both of these data types to be combined in such a way that they can be analysed side-by-side. This paper describes an end-to-end system for the collection, analysis, storage and visualisation of qualitative and quantitative data, developed using the e-Science Central cloud analytics platform. We describe the experience of developing the system, based on a case study that involved collecting data about the built environment and its users. In this case study, data is collected from older adults living in residential care. Sensors were placed throughout the care home and smart devices were issued to the residents. This sensor data is uploaded to the analytics platform and the processed results are stored in a data warehouse, where it is integrated with qualitative data collected by healthcare and architecture researchers. Visualisations are also presented which were intended to allow the data to be explored and for potential correlations between the quantitative and qualitative data to be investigated.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129275583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introducing the Physiome Journal: Improving Reproducibility, Reuse, and Discovery of Computational Models","authors":"D. Nickerson, P. Hunter","doi":"10.1109/eScience.2017.65","DOIUrl":"https://doi.org/10.1109/eScience.2017.65","url":null,"abstract":"In August 2017 under the auspices of the International Union of Physiological Sciences (IUPS), we will launch a new journal: Physiome. The goal of Physiome is to promote, encourage, and support the wide-spread adoption of technologies and workflows that generally improve the ability of scientists to discover existing computational models which are relevant to their work, reproduce the predictions of those models, and understand the scope, limitations, and provenance of the models in order to appropriately reuse suitable models in the quest to address their own hypotheses. As we prepare for the launch, we present here the current status and future plans for Physiome.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129230249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mid-level Image Representation for Fruit Fly Identification (Diptera: Tephritidae)","authors":"Matheus Macedo Leonardo, S. Avila, R. Zucchi, F. Faria","doi":"10.1109/eScience.2017.33","DOIUrl":"https://doi.org/10.1109/eScience.2017.33","url":null,"abstract":"Fruit flies are of huge biological and economic importance for the farming of different countries in the World, especially for Brazil. Brazil is the third largest fruit producer in the world with 44 million tons in 2016. The direct and indirect losses caused by fruit flies can exceed USD 2 billion, putting these pests as one of the biggest problems of the world agriculture. In Brazil, it is estimated that the economic losses directly related to production, the cost of pest control and in the loss of export markets, are between USD 120 and 200 million/year. The species of the genus Anastrepha are among the fruit flies economically important in the America tropics and subtropics with approximately 300 known species, of which 120 are recorded in Brazil. However, few species are economically important in Brazil and are considered pests of quarantine significance by regulatory agencies. In this sense, the development of automatic and semi-automatic tools for fruit fly species identification of the genus Anastrepha can assist the few existing specialists to reduce the insect analysis time and the economic losses related to these agricultural pests. We propose to apply mid-level image representations based on local descriptors for fruit fly identification tasks of three species of the genus Anastrepha. In our experiments, several local image descriptors based on keypoints and machine learning techniques have been studied for the target task. Furthermore, the proposed approaches have achieved excellent effectiveness results when compared with a state-of-art technique.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116365340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Provenance for Multi-Agent Models","authors":"Delmar B. Davis, J. Featherston, M. Fukuda, Hazeline U. Asuncion","doi":"10.1109/eScience.2017.16","DOIUrl":"https://doi.org/10.1109/eScience.2017.16","url":null,"abstract":"Multi-agent simulations are useful for exploring collective patterns of individual behavior in social, biological, economic, network, and physical systems. However, there is no provenance support for multi-agent models (MAMs) in a distributed setting. To this end, we introduce ProvMASS, a novel approach to capture provenance of MAMs in a distributed memory by combining inter-process identification, lightweight coordination of in-memory provenance storage, and adaptive provenance capture. ProvMASS is built on top of the Multi-Agent Spatial Simulation (MASS) library, a framework that combines multi-agent systems with large-scale fine-grained agent-based models, or MAMs. Unlike other environments supporting MAMs, MASS parallelizes simulations with distributed memory, where agents and spatial data are shared application resources. We evaluate our approach with provenance queries to support three use cases and performance measures. Initial results indicate that our approach can support various provenance queries for MAMs at reasonable performance overhead.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121054256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"dSpark: Deadline-Based Resource Allocation for Big Data Applications in Apache Spark","authors":"Muhammed Tawfiqul Islam, S. Karunasekera, R. Buyya","doi":"10.1109/eScience.2017.21","DOIUrl":"https://doi.org/10.1109/eScience.2017.21","url":null,"abstract":"Large-scale data processing framework like Apache Spark is becoming more popular to process large amounts of data either in a local or a cloud deployed cluster. When an application is deployed in a Spark cluster, all the resources are allocated to it unless users manually set a limit on the available resources. In addition, it is not possible to impose any user-specific constraints and minimize the cost of running applications. In this paper, we present dSpark, a lightweight, pluggable resource allocation framework for Apache Spark. In dSpark, we have modelled the application completion time with respect to the number of executors and application input/iteration. This model is further used in our proposed resource allocation model where a deadlinebased, cost-efficient resource allocation scheme can be selected for any application. As opposed to the existing frameworks that focus more on modelling the number of VMs to use for an application, we have modelled both the application cost and completion time with respect to executors, hence providing a finegrained resource allocation scheme. In addition, users do not need to specify any application types in dSpark. We have evaluated our proposed framework through extensive experimentation, which shows significant performance benefits. The application completion time prediction model has a mean relative error (RE) less than 7% for different types of applications. Furthermore, we have shown that our proposed resource allocation model minimizes the cost of running applications and selects effective resource allocation schemes under varying user-specific deadlines.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124979191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Using Citizen Scientists to Drive Automated Ecological Object Detection in Aerial Imagery","authors":"Connor Bowley, Marshall Mattingly, Andrew F. Barnas, Susan N. Ellis‐Felege, Travis Desell","doi":"10.1109/eScience.2017.22","DOIUrl":"https://doi.org/10.1109/eScience.2017.22","url":null,"abstract":"Automated object detection within imagery is challenging in the field of wildlife biology. Uncontrolled conditions, along with the relative size of target species to the more abundant background makes manual detection tedious and error-prone. In order to address these concerns, the Wildlife@Home project has been developed with a web portal to allow citizen scientists to inspect and catalog these images, which in turn provides training data for computer vision algorithms to automate the detection process. This work focuses on a project with over 65,000 Unmanned Aerial System (UAS) images from flights in the Hudson Bay area of Canada gathered in the years 2015 and 2016. This data set comprises over 3TB of raw imagery and also contains a further 2 million images from related ecological projects. Given the data scale, the person-hours that would be needed to manually inspect the data is extremely high. This work examines the efficacy of using citizen science data as inputs to convolutional neural networks (CNNs) used for object detection. Three CNNs were trained with expert observations, citizen scientist observations, and matched observations made by pairing citizen scientist observations of the same object and taking the intersection of the two observations. The expert, matched, and unmatched CNNs overestimated the number of lesser snow geese in the testing images by 88%, 150%, and 250%, respectively, which is less than current work using similar techniques on all visible (RGB) UAS imagery. These results show that the accuracy of the input data is more important than the quantity of the input data, as the unmatched citizen scientists observations are shown to be highly variable, but substantial in number, while the matched observations are much closer to the expert observations, though less in number. To increase the accuracy of the CNNs, it is proposed to use a feedback loop to ensure the CNN gets continually trained using extracted observations that it did poorly on during the testing phase.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117301454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TaRDIS, a Visual Analytics System for Spatial and Temporal Data in Archaeo-Related Disciplines","authors":"Daniel Kaltenthaler, Johannes-Y. Lohrer, Ptolemaios D. Paxinos, Dan Hammerle, Henriette Obermaier, Peer Kröger","doi":"10.1109/eScience.2017.48","DOIUrl":"https://doi.org/10.1109/eScience.2017.48","url":null,"abstract":"In this paper, we describe the application TaRDIS, a visual analytics system for spatial and temporal data designed for the needs of archaeo-related disciplines that supports domain experts in analyzing their data. The temporal data is visualized in form of an interactive Harris Matrix that illustrates the temporal position of the layers. The 2D and 3D visualization sketches the spatial position of findings with heat map colors and a Kernel Density Estimation. The application allows the visual comparison of the temporal and spatial data. We discuss the archaeological background followed by the technical implementation of the application and the generation of the Harris Matrix. Finally, we demonstrate the usefulness of the application by analyzing real zooarchaeological data from an excavation where we use TaRDIS to show the distribution of animal species in a case study.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128010820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing a High-Throughput Pipeline for Digitizing Pinned Insects","authors":"M. Hereld, N. Ferrier, Nitin Agarwal, P. Sierwald","doi":"10.1109/eScience.2017.88","DOIUrl":"https://doi.org/10.1109/eScience.2017.88","url":null,"abstract":"This paper presents the design and prototyping of hardware and software to address the problem of rapid and reliable 3D digitization of very large collections of pinned insects. Using the collection at the Field Museum of Natural History (FMNH) as a use case, a pipeline to ingest the entire collection of 4.5 million specimens in circa 1-2 years imposes a few second limit on average processing time per specimen. We describe the design and implementation of multi-camera systems capable of rapidly capturing light field imagery for 3D reconstruction of label surfaces and specimen in single snapshots consistent with this time constraint. With imagery captured using these prototype multi-cameras we demonstrate methods under development for 3D reconstruction of pinned insect specimens and for processing text on label surfaces.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130235786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}