Abstract
Nowadays using E-learning platforms such as Intelligent Tutoring Systems (ITS) that support users to learn subjects are quite common. Despite the availability and the advantages of these systems, they ignore the learners' time limitation for learning a subject. In this paper we propose RUTICO, that recommends successful learning paths with respect to a learner's knowledge background and under a time constraint. RUTICO, which is an example of Long Term goal Recommender Systems (LTRS), a.er locating a learner in the course graph, it utilizes a Depth-first search (DFS) algorithm to find all possible paths for a learner given a time restriction. RUTICO also estimates learning time and score for the paths and finally, it recommends a path with the maximum score that satisfies the learner time restriction. In order to evaluate the ability of RUTICO in estimating time and score for paths, we used the Mean Absolute Error and Error. Our results show that we are able to generate a learning path that maximizes a learner's score under a time restriction. © 2017 ACM.

Abstract
We demonstrate in this research that surface-enhanced resonance Raman scattering combined with Raman imaging can be effectively used for analysis of distinct forms of organic dyes in antimicrobial Ag-loaded textile fibers. The potential of this approach, as a non-destructive characterization method of fabrics, was evaluated with Raman studies performed on the molecular forms of methylene blue (MB), used here as the organic dye model. On the basis of the surface-enhanced Raman scattering spectra of MB monomers and dimers, the Raman imaging of Ag-loaded linen fibers previously treated with MB solution was performed and then used for identification of the adsorbate species in distinct regions of the substrates. A semi-quantitative analysis is then performed by considering the area of the Raman bands ascribed to the MB molecular forms and image analysis applied to Raman images. Copyright (c) 2017 John Wiley & Sons, Ltd.

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Abstract
The class imbalance problem is a key issue that has received much attention. This attention has been mostly focused on two-classes problems. Fewer solutions exist for the multi-classes imbalance problem. From an evaluation point of view, the class imbalance problem is challenging because a non-uniform importance is assigned to the classes. In this paper, we propose a relevance-based evaluation framework that incorporates user preferences by allowing the assignment of differentiated importance values to each class. The presented solution is able to overcome difficulties detected in existing measures and increases discrimination capability. The proposed framework requires the assignment of a relevance score to the problem classes. To deal with cases where the user is not able to specify each class relevance, we describe three mechanisms to incorporate the existing domain knowledge into the relevance framework. These mechanisms differ in the amount of information available and assumptions made regarding the domain. They also allow the use of our framework in common settings of multi-class imbalanced problems with different levels of information available. © 2017, Springer International Publishing AG.