Abstract
Existing literature argues that emotions have a significant impact on the majority of human activities and functions. The learning process is one of the activities on which emotions have a direct influence. Thus, understanding the manner in which emotions change the students' learning process is not only very important but it can also allow to improve the existing learning models. Currently, in the majority of situations, the teacher serves as a facilitator between the student and the learning course, and through a constant analysis of the student's behaviour, emotions and achievements, he constantly performs adjustments to the teaching process in order to meet the students' needs and goals. Thus far, in online learning environments there is no easy way for teachers to analyse students' behaviour and emotions. A possible solution to this problem can be the development of mechanisms that enable computers to automatically detect students' emotions and adapt the learning process in order to meet students' real needs. An emotional learning model was described and a software prototype was developed and tested, in order to find out whether it performs live identification of the students' emotions, by using affective computing techniques, and whether it automatically performs adjustments to their individual learning process. Through a deeper analysis and multi-disciplinary discussion of the achieved results it is possible to acknowledge that not only emotions impact students' learning, but also that an application that performs live emotion recognition and which integrates this feature with adjustable online learning environments will trigger improvements in students' learning.

Abstract
Cervical cancer remains a significant cause of mortality in low-income countries. As in many other diseases, the existence of several screening/diagnosis methods and subjective physician preferences creates a complex ecosystem for automated methods. In order to diminish the amount of labeled data from each modality/expert we propose a regularization-based transfer learning strategy that encourages source and target models to share the same coefficient signs. We instantiated the proposed framework to predict cross-modality individual risk and cross-expert subjective quality assessment of colposcopic images for different modalities. Thus, we are able to transfer knowledge gained from one expert/modality to another.

Abstract
The use of Fourier methods in wave-front reconstruction can significantly reduce the computation time for large telescopes with a high number of degrees of freedom. However, Fourier algorithms for discrete data require a rectangular data set which conform to specific boundary requirements, whereas wave-front sensor data is typically defined over a circular domain (the telescope pupil). Here we present an iterative Gerchberg routine modified for the purposes of discrete wave-front reconstruction which adapts the measurement data (wave-front sensor slopes) for Fourier analysis, fulfilling the requirements of the fast Fourier transform (FFT) and providing accurate reconstruction. The routine is used in the adaptation step only and can be coupled to any other Wiener-like or least-squares method. We compare simulations using this method with previous Fourier methods and show an increase in performance in terms of Strehl ratio and a reduction in noise propagation for a 40x40 SPHERE-like adaptive optics system. For closed loop operation with minimal iterations the Gerchberg method provides an improvement in Strehl, from 95.4% to 96.9% in K-band. This corresponds to ~ 40 nm improvement in rms, and avoids the high spatial frequency errors present in other methods, providing an increase in contrast towards the edge of the correctable band.

Abstract
Introducing Combined Heat and Power (CHP) units into Active Distribution Network (ADN) can significantly affect the problem of optimal generation scheduling. A new method for solving the problem of Optimal Scheduling of Combined Heat and Power (OSCHP) units of an ADN with Electric Storage Systems (ESSs) and Thermal Storage Systems (TSSs) considering Industrial Customers (ICs) Inter-Zonal Power Exchanges (IZPEs) is presented. The ADN operator may use CHP units to supply its ICs and based on smart grid conceptual model, it can transact electricity with upstream network. However, the electricity transactions between the ADN and its ICs in normal and contingency scenarios may highly complicate this problem. In this paper, linearization techniques are adopted to linearize equations and a two-stage stochastic mixed integer linear programming (SMILP) model is utilized to solve the problem to determine the optimal generation scheduling units. The first stage models the behaviour of operation parameters, minimizes the operation costs, and checks the feasibility of the ICs' requested firm and non firm IZPEs, while the second stage considers system's stochastic contingency scenarios. The competitiveness of ADN in the deregulated market can be improved by adjusting the proposed decision variables in the two-stage optimization procedure. The proposed method is applied to 18- and 123-bus IEEE test systems to thoroughly demonstrate the benefits of implementing inter-zonal power exchanges.

Abstract

Abstract
The increase in usage of smartphones and the ubiquity of Internet access have made mobile communications services very attractive to users. Messaging services are among the most popular services on the Internet. In recent years, this services started to support confidentiality and anonymity. A recurrent problem with the existing messaging solutions is their lack of resistance to impersonation attacks. The proposed solution addresses the impersonation problem, without neglecting user confidentiality and anonymity, by forcing users to exchange the required cryptographic material among themselves. Moreover, this exchange must use a proximity communication technology, forcing the users to physically meet.