Carsten Lecon1 and Marc Hermann2, 1Department of Computer Science, Media Computer Science, Aalen University, Germany and 2Department of Computer Science, User Experience, Aalen University, Germany
In response to the heterogeneity of previous knowledge of the students when beginning their studies, we present a solution, where undergraduate students as well as advanced students (hopefully) will benefit from ‘AdLeR’ (Additive Learning Resources): A tool for the rapid generation of small e-learning courses. The undergraduates can catch up lack of knowledge by our mini courses (self-regulated). The advanced students are involved in the development of our tool or in the creation process of learning material, which is suited for self-regulated learning. When implementing the tool, the students have to deal with various aspects of computer science domains for example, which consolidates their knowledge and their competences.
E-Learning, self-regulated learning, learning by teaching, XML, learning path, search functionality.
Gaetano Zazzaro, Software Development, Information Management and HPC Lab, CIRA (Italian Aerospace Research Centre), Capua (CE), Italy
The class imbalance problem is widespread in Data Mining and it can reduce the general performance of a classification model. Many techniques have been proposed in order to overcome it, thanks to which a model able to handling rare events can be trained. The methodology presented in this paper, called Controlled Over-Sampling Method (COSM), includes a controller model able to reject new synthetic elements for which there is no certainty of belonging to the minority class. It combines the common Machine Learning method for holdout with an oversampling algorithm, for example the classic SMOTE algorithm. The proposal explained and designed here represents a guideline for the application of oversampling algorithms, but also a brief overview on techniques for overcoming the problem of the unbalanced class in Data Mining.
Class imbalance problem, Data Mining, Holdout Method, Oversampling, Rare Class Mining, Undersampling
Malobika Roy Choudhury, Innovation and Technology, SAP Labs India Pvt Lmt., Bengaluru, Karnataka, India
Every product industry goes through the process of product validation before its release. Validation could be effortless or laborious depending upon the process. Here in this paper, we define a process that can make the task-independent of constant monitoring. This method will not only make the work of test engineers easier it will also help the company meet stringent release deadlines with ease. Our method explores how to complete visual validation of the display screen using deep learning and image processing. In our example, we have applied the method over a car-cluster display screen. Our method breaks down the components of the screen then validate each component against its design and outputs a result predicting whether the displayed content is correct or incorrect. We are using models of YOLOv, Machine Learning (Positional value approximation), CNN, and few image processing techniques to predict the accuracy of each display component. These sets of algorithms compile to provide consistent results throughout and are being currently used to generate results for the validation process.
CNN, YOLO, display-validation
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