Open production communities (OPCs) provide technical features and social norms for a vast but dispersed and diverse crowd to collectively accumulate content. In OPCs, certain mechanisms, policies, and technologies are provided for voluntary users to participate in community-related activities including content generation, evaluation, qualification, and distribution and in some cases even community governance. Due to the known complexities and dynamism of online communities, designing a successful community is deemed more an art than a science. Numerous studies have investigated different aspects of certain types of OPCs. Most of these studies, however, fall short of delivering a general view or prescription due to their narrow focus on a certain type of OPCs. In contribution to theories on technology-mediated social participation (TMSP), this study synthesizes the streams of research in the particular area of OPCs and delivers a theoretical framework as a baseline for adapting findings from one specific type of community on another. This framework consists of four primary dimensions, namely, platform features, content, user, and community. The corresponding attributes of these dimensions and the existing interdependencies are discussed in detail. Furthermore, a decision diagram for selecting features and a design guideline for "decontextualizing" findings are introduced as possible applications of the framework. The framework also provides a new and reliable foundation on which future research can extend its findings and prescriptions in a systematic way. ACM Reference Format:Pujan Ziaie. 2014. A model for context in the design of open production communities.

We present an effective and fast method for static hand gesture recognition. This method is based on classifying the different gestures according to geometric-based invariants which are obtained from image data after segmentation; thus, unlike many other recognition methods, this method is not dependent on skin color. Gestures are extracted from each frame of the video, with a static background. The segmentation is done by dynamic extraction of background pixels according to the histogram of each image. Gestures are classified using a weighted K-Nearest Neighbors Algorithm which is combined with a nave Bayes approach to estimate the probability of each gesture type.

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In this paper, a reliable, fast and robust approach for static hand gesture recognition in the domain of a Human-Robot interaction system is presented. The method is based on computing the likelihood of different existing gesture-types and assigning a probability to every type by using Bayesian inference rules. For this purpose, two classes of geometrical invariants has been defined and the gesture likelihoods of both of the invariant-classes are estimated by means of a modified K-Nearest Neighbors classifier. One of the invariant-classes consists of the well-known Hu moments and the other one encompasses five defined geometrical attributes that are transformation, rotation and scale invariant, which are obtained from the outer-contour of a hand. Given the experimental results of this approach in the domain of the Joint-Action Science and Technology (JAST) project, it appears to have a very considerable performance of more than 95% correct classification results on average for three types of gestures (pointing, grasping and holding-out) under various lighting conditions and hand poses.