Improved Vote Aggregation Techniques for the Geo-Wiki Cropland Capture Crowdsourcing Game

Crowdsourcing is a new approach for solving data processing problems for which conventional methods appear to be inaccurate, expensive, or time-consuming. Nowadays, the development of new crowdsourcing techniques is mostly motivated by so called Big Data problems, including problems of assessment and clustering for large datasets obtained in aerospace imaging, remote sensing, and even in social network analysis. By involving volunteers from all over the world, the Geo-Wiki project tackles problems of environmental monitoring with applications to flood resilience, biomass data analysis and classification of land cover. For example, the Cropland Capture Game, which is a gamified version of Geo-Wiki, was developed to aid in the mapping of cultivated land, and was used to gather 4.5 million image classifications from the Earth’s surface. More recently, the Picture Pile game, which is a more generalized version of Cropland Capture, aims to identify tree loss over time from pairs of very high resolution satellite images. Despite recent progress in image analysis, the solution to these problems is hard to automate since human experts still outperform the majority of machine learning algorithms and artificial systems in this field on certain image recognition tasks. The replacement of rare and expensive experts by a team of distributed volunteers seems to be promising, but this approach leads to challenging questions such as: how can individual opinions be aggregated optimally, how can confidence bounds be obtained, and how can the unreliability of volunteers be dealt with? In this paper, on the basis of several known machine learning techniques, we propose a technical approach to improve the overall performance of the majority voting decision rule used in the Cropland Capture Game. The proposed approach increases the estimated consistency with expert opinion from 77% to 86%

How to Increase the Accuracy of Crowdsourcing Campaigns?

Crowdsourcing is a new approach to performing tasks, with a group of volunteers rather than experts. For example, the Geo-Wiki project [1] aims to improve the global land-cover map by crowdsourcing for image recognition. Though crowdsourcing gives a simple way to perform tasks that are hard to automate, analysis of data received from non-experts is a challenging problem that requires a holistic approach. Here we study in detail the dataset of the Cropland Capture game (part of Geo-Wiki project) to increase the accuracy of campaign’s results. Using this analysis, we developed a methodology for a generic type of crowdsourcing campaign similar to the Cropland Capture game. The proposed methodology relies on computer vision and machine learning techniques. Using the Cropland Capture dataset we showed that our methodology increases agreement between aggregated volunteers’ votes and experts’ decisions from 77% to 86%. [1] Fritz, Steffen, et al. “Geo-Wiki. Org: The use of crowdsourcing to improve global land cover.” Remote Sensing 1.3 (2009): 345-354

The implementation of the cluster approach in the regional system of vocational education

© 2015, Review of European Studies. All right reserved. The relevance of the problem stems from the fact that the implementation of the cluster approach in the regional system of vocational education aimed at the maintenance and development of joint potential opportunities of industrial and educational spheres, which create prospects for progressive development of the Tatarstan Republic. This article aims to identify the features of the cluster approach in the regional system of vocational training that focus on strengthening the relationship network between the cluster members in order to facilitate access to new industrial and educational technologies, to share knowledge and fixed-capital assets, to accelerate learning processes due to continuity and integration processes in a single integrated regional educational-industrial complex. The article stuff is valuable for managers of vocational training institutions and representatives of the industrial sector in order to prepare specialists meeting the requirements of modern production

METHODICAL APPROACHES TO FORMING INNOVATIVE PROJECT AND ENTERPRISE MANAGEMENT ORGANIZATIONAL STRUCTURES

The following principles are discussed as methodical bases of the choice of innovative project and enterprise management organizational structures: hierarchism, bureaucratization, adhocratie, tent organization, lean business engineering, rational organization, amorphous organization, teaching organization. Conditions that complicate industrial and technological processes influence enterprise management process dynamics. The variety of existing types of organizational structures is determined by industrial, economic, psychological and social conditions

Superoutburst of a New Sub-Period-Minimum Dwarf Nova CSS130418 in Hercules

Multicolour photometry of a new dwarf nova CSS130418 in Hercules, which underwent superoutburst on April 18, 2013, allow to classified it as a WZ Sge-type dwarf nova. The phase light curves for different stages of superoutburst are presented. The early superhumps were used to determine the orbital period Porb = 64.84(1) minutes, which is shorter than the period minimum ~78 minutes for normal hydrogen-rich cataclysmic variables. We found the mean period of ordinary superhumps Psh = 65.559(1) minutes. The quiescent spectrum is rich in helium, showing double peaked emissionlines of H I and He I from accretion disk, so the dwarf nova is in a late stage of stellar evolution