As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called 'reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135.
In-mold assembly can be used to create plastic products with articulated joints. This process eliminates the need for post-molding assembly and reduces the number of parts being used in the product, hence improving the product quality. However, designing both products and molds is significantly more challenging in case of in-mold assembly. Currently, a systematic methodology does not exist for developing product and processes to exploit potential benefits of in-mold assembly for creating articulated joints. This paper is a step towards creating such a methodology and reports the following three results. First, it presents a model for designing assemblies and molding process so that the joint clearances and variation in the joint clearances can meet the performance goals. Second, it describes proven mold design templates for realizing revolute, prismatic, and spherical joints. Third, it describes a mold design methodology for designing molds for products that contain articulated joints and will be produced using in-mold assembly process. Three case studies are also presented to illustrate how in-mold assembly process can be used to create articulated devices.
Abstract. The similarity join has become an important database primitive to support similarity search and data mining. A similarity join combines two sets of complex objects such that the result contains all pairs of similar objects. Well-known are two types of the similarity join, the distance range join where the user defines a distance threshold for the join, and the closest point query or k-distance join which retrieves the k most similar pairs. In this paper, we propose an important, third similarity join operation called k-nearest neighbor join which combines each point of one point set with its k nearest neighbors in the other set. We discover that many standard algorithms of Knowledge Discovery in Databases (KDD) such as k-means and k-medoid clustering, nearest neighbor classification, data cleansing, postprocessing of sampling-based data mining etc. can be implemented on top of the k-nn join operation to achieve performance improvements without affecting the quality of the result of these algorithms. Our list of possible applications includes standard methods for all stages of the KDD process including preprocessing, data mining, and postprocessing. Thus, our method is turbo charging the complete KDD process.
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