Abstract
Most business enterprises are small and medium-sized enterprises (SMEs), and many of them are without a human resource and recruitment department. Thus, one of the challenges that organizations currently face is to find a strategy to retain and attract talent. To overcome this difficulty, enterprises must invest in employer branding policies and be aware of the factors that differentiate them from others when attracting employees. This study aims to develop an employer branding model applicable to SMEs, to increase and enhance the attraction and retention of talents. An exploratory approach based on a quantitative perspective was adopted to develop an employer branding model applied to SMEs, with two major reference employer branding models and frameworks used as the main support. The model of employer branding was applied to SMEs regarding four dimensions, whereby essential questions are asked, namely (1) organizational culture (e.g., do employees have a job description aligned with the corporate culture?), (2) company strategy (e.g., what is the strategy if business volume decreases?), (3) company reputation (e.g., how do you perceive and treat negative feedback?), and (4) reward systems (e.g., do you feel that your employees are motivated intrinsically or extrinsically or both?), ordered by layers based on a logical sequence. The new proposed model is expected to serve as a useful strategic tool and as a basis for attracting, retaining and managing talent, specifically in the SMEs context. This new model provides a set of strategic and competitiveness benefits for SMEs, while contributing to making enterprises more profitable. The model also contributes to SMEs having a better image and reputation, enabling them to stand out from others in the war for talent.

Abstract
The acquisition of competences for the development of computer programs is one of the main challenges faced by computer science students. As a result of not being able to develop the abilities needed (for example, abstraction), students drop out the subjects and sometimes even the course. There is a need to study the causes of student success (or failure) in introductory curricular units to check for behaviours or characteristics that may be determinant and thus try to prevent and change said causes. The students of one programming curricular unit were invited to answer four surveys. We use machine learning techniques to try to predict the students' grades based on the answers obtained on the surveys. The results obtained enable us to plan the semester accordingly, by anticipating how many students might need extra support. We hope to increase the students' motivation and, with this, increase their interest on the subject. This way we aim to accomplish our ultimate goal: reducing the drop out and increasing the overall average student performance.

Abstract

Abstract
The performance evaluation of education systems is at the top of the agenda of governments and education authorities worldwide. However, research involving cross-country comparisons of the performance of education systems is still incipient. This paper proposes a new composite indicator to summarise the performance of education systems, enabling benchmarking comparisons and the definition of objectives for improvement. The research analyses different modelling alternatives for the construction of composite indicators, with varying degrees of weight flexibility. Our study uses annual data of 29 European countries, collected from Eurostat and the Organisation for Economic Co-operation and Development databases. The results obtained in terms of performance scores and country rankings are presented and their managerial implications are discussed. We conclude that composite indicators estimated using frontier techniques can support the transition from the paradigm of performance assessment (control) to performance management (improvement).

Abstract
Successful software systems tend to grow considerably, ending up suffering from essential complexity, and very hard to understand as a whole. Software visualization techniques have been explored as one approach to ease software understanding. This work presents a novel approach and environment for software development that explores the use of liveness and virtual reality (VR) as a way to shorten the feedback loop between developers and their software systems in an interactive and immersive way. As a proof-of-concept, the authors developed a prototype that uses a visual city metaphor and allows developers to visit and dive into the system, in a live way. To assess the usability and viability of the approach, the authors carried on experiments to evaluate the effectiveness of the approach, and how to best support a live approach for software development.

Abstract
The increasing integration of Distributed Energy Resources (DER) in electricity networks has required an improvement in the network management procedures. While the operation paradigm is evolving and adapting to the new network features, the planning approach is rather inefficient as network assets are usually oversized to meet the worst-case scenario. In this regard, this paper presents an innovative methodology that integrates the potential flexibility of DER into the planning process, in an attempt to bridge the gap between current network operation approaches and the planning methods. It includes an analysis of future scenarios, providing different reinforcement plans considering the realistic network operation for those scenarios. The proposed optimal design of the reinforcement plans has two complementary processes: First to optimize flexible resources in their owner's perspective and second to reschedule the flexible resources' operation when the DSO needs to solve technical problems. The model has been tested in a typical Portuguese medium voltage network using future scenarios of DER integration from ENTSO-E. The results conclude that the proposed methodology leads to cost-effective solutions, which provide a better use of flexible resources, deferring high capital investments in network reinforcement.