12 research outputs found
Qualitative Case Study Methodology: Automatic Design and Correction of Ceramic Colors
This paper is focused on two major problems within the ceramics industry. The reproduction of a desired color from pigments (which is time-intensive), and the correction of colors on the production line (which is costly) are processes which still rely heavily on numerous experiments carried out by human operators. This study presents the key aspects of these two processes and suggests some mathematical and computer sciences tools, aimed at automatizing the current procedures. Data was provided by an industrial partner, where the proposed models and solutions will be tested and validated
Expectation Propagation for Rating Players in Sports Competitions
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34811.pdf (preprint version ) (Open Access
Bayesian Monte Carlo for the global optimization of expensive functions
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83325.pdf (preprint version ) (Open Access
Multi-task preference learning with an application to hearing aid personalization
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83902.pdf (preprint version ) (Open Access)9 p
Towards Mapping Images to Text Using Deep-Learning Architectures
Images and text represent types of content that are used together for conveying a message. The process of mapping images to text can provide very useful information and can be included in many applications from the medical domain, applications for blind people, social networking, etc. In this paper, we investigate an approach for mapping images to text using a Kernel Ridge Regression model. We considered two types of features: simple RGB pixel-value features and image features extracted with deep-learning approaches. We investigated several neural network architectures for image feature extraction: VGG16, Inception V3, ResNet50, Xception. The experimental evaluation was performed on three data sets from different domains. The texts associated with images represent objective descriptions for two of the three data sets and subjective descriptions for the other data set. The experimental results show that the more complex deep-learning approaches that were used for feature extraction perform better than simple RGB pixel-value approaches. Moreover, the ResNet50 network architecture performs best in comparison to the other three deep network architectures considered for extracting image features. The model error obtained using the ResNet50 network is less by approx. 0.30 than other neural network architectures. We extracted natural language descriptors of images and we made a comparison between original and generated descriptive words. Furthermore, we investigated if there is a difference in performance between the type of text associated with the images: subjective or objective. The proposed model generated more similar descriptions to the original ones for the data set containing objective descriptions whose vocabulary is simpler, bigger and clearer
A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions
Multi-Domain Transfer Component Analysis for Domain Generalization
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179118.pdf (Publisher’s version ) (Open Access