Mind the Gap: From Desktop to App

In this article we present a new mobile game, edugames4all MicrobeQuest!, that covers core learning objectives from the European curriculum on microbe transmission, food and hand hygiene, and responsible antibiotic use. The game is aimed at 9 to 12 year olds and it is based on the desktop version of the edugames4all platform games. We discuss the challenges and lessons learned transitioning from a desktop based game to a mobile app. We also present the seamless evaluation obtained by integrating the assessment of educa- tional impact of the game into the game mechanics

FEMwiki: crowdsourcing semantic taxonomy and wiki input to domain experts while keeping editorial control: Mission Possible!

Highly specialized professional communities of practice (CoP) inevitably need to operate across geographically dispersed area - members frequently need to interact and share professional content. Crowdsourcing using wiki platforms provides a novel way for a professional community to share ideas and collaborate on content creation, curation, maintenance and sharing. This is the aim of the Field Epidemiological Manual wiki (FEMwiki) project enabling online collaborative content sharing and interaction for field epidemiologists around a growing training wiki resource. However, while user contributions are the driving force for content creation, any medical information resource needs to keep editorial control and quality assurance. This requirement is typically in conflict with community-driven Web 2.0 content creation. However, to maximize the opportunities for the network of epidemiologists actively editing the wiki content while keeping quality and editorial control, a novel structure was developed to encourage crowdsourcing – a support for dual versioning for each wiki page enabling maintenance of expertreviewed pages in parallel with user-updated versions, and a clear navigation between the related versions. Secondly, the training wiki content needs to be organized in a semantically-enhanced taxonomical navigation structure enabling domain experts to find information on a growing site easily. This also provides an ideal opportunity for crowdsourcing. We developed a user-editable collaborative interface crowdsourcing the taxonomy live maintenance to the community of field epidemiologists by embedding the taxonomy in a training wiki platform and generating the semantic navigation hierarchy on the fly. Launched in 2010, FEMwiki is a real world service supporting field epidemiologists in Europe and worldwide. The crowdsourcing success was evaluated by assessing the number and type of changes made by the professional network of epidemiologists over several months and demonstrated that crowdsourcing encourages user to edit existing and create new content and also leads to expansion of the domain taxonomy

Using demographics toward efficient data classification in citizen science: a Bayesian approach

Public participation in scientific activities, often called citizen science, offers a possibility to collect and analyze an unprecedentedly large amount of data. However, diversity of volunteers poses a challenge to obtain accurate information when these data are aggregated. To overcome this problem, we propose a classification algorithm using Bayesian inference that harnesses diversity of volunteers to improve data accuracy. In the algorithm, each volunteer is grouped into a distinct class based on a survey regarding either their level of education or motivation to citizen science. We obtained the behavior of each class through a training set, which was then used as a prior information to estimate performance of new volunteers. By applying this approach to an existing citizen science dataset to classify images into categories, we demonstrate improvement in data accuracy, compared to the traditional majority voting. Our algorithm offers a simple, yet powerful, way to improve data accuracy under limited effort of volunteers by predicting the behavior of a class of individuals, rather than attempting at a granular description of each of them

When in doubt ask the crowd : leveraging collective intelligence for improving event detection and machine learning

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An Examination of the Work Practices of Crowdfarms

Crowdsourcing is a new value creation business model. Annual revenue of the Chinese market alone is hundreds of millions of dollars, yet few studies have focused on the practices of the Chinese crowdsourcing workforce, and those that do mainly focus on solo crowdworkers. We have extended our study of solo crowdworker practices to include crowdfarms, a relatively new entry to the gig economy: small companies that carry out crowdwork as a key part of their business. We report here on interviews of people who work in 53 crowdfarms. We describe how crowdfarms procure jobs, carry out macrotasks and microtasks, manage their reputation, and employ different management practices to motivate crowdworkers and customers

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method