Deep learning methods are becoming more popular for complex pattern recognition applications. As result, many frameworks have appeared aiming to facilitate the development of such applications. However, choosing a suitable framework may not be an easy task for new users. In this paper, a qualitative evaluation of four of the most popular Deep Learning frameworks is provided, including: Caffe, Torch, Lasagne and TensorFlow. A printed character recognition task was used as case study, and a Convolutional Neural Network was implemented for this purpose. The analysis focus on issues that are important for the development process and encompasses nine qualitative dimensions, showing the strengths and weaknesses of each framework. It is expected that this analysis can be useful for guiding new users in the area.
This paper proposes PredicTour, an approach to process check-ins made by users of locationbased social networks (LBSNs), and predict mobility patterns of tourists visiting new countries with or without previous visiting records. PredicTour is composed of three key parts: mobility modeling, profile extraction, and tourist mobility prediction. In the first part, sequences of check-ins within a time interval are associated with other user information to produce a new structure called "mobility descriptor". In the profile extraction, self-organizing maps and fuzzy C-means work jointly to group users according to their mobility descriptors. PredicTour then identifies tourist profiles and estimates mobility patterns of tourists visiting new countries. When comparing the performance of PredicTour with three well-known machine learning-based models, the results indicate that PredicTour outperforms the baseline approaches. Therefore, it is a good alternative for predicting and understanding international tourists' mobility. The proposed approach can be used in different applications, such as in recommender systems for tourists or in decision-making support for urban planners interested in improving tourists' experiences.
Automatic short answer grading is the study field that addresses the assessment of students’ answers to questions in natural language. The grading of the answers is generally seen as a typical classification supervised learning. To stimulate research in the field, two datasets were publicly released in the SemEval 2013 competition task “Student Response Analysis”. Since then, some works have been developed to improve the results. In this context, the goal of this work is to tackle such task by implementing lessons learned from the literature in an effective way and report results for both datasets and all of its scenarios. The proposed method obtained better results in most scenarios of the competition task and, therefore, higher overall scores when compared to recent works.
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