Abstract
We describe our experience using magic card tricks to teach algorithmic skills to first-year Computer Science undergraduates. We illustrate our approach with a detailed discussion on a card trick that is typically presented as a test to the psychic abilities of an audience. We use the trick to discuss concepts like problem decomposition, pre- and post-conditions, and invariants. We discuss pedagogical issues and analyse feedback collected from students. The feedback has been very positive and encouraging. © 2014 ACM.

Abstract
Discovery of communities in complex networks is a fundamental data analysis problem with applications in various domains. While most of the existing approaches have focused on discovering communities of nodes, recent studies have shown the advantages and uses of link community discovery in networks. Generative models provide a promising class of techniques for the identification of modular structures in networks, but most generative models mainly focus on the detection of node communities rather than link communities. In this work, we propose a generative model, which is based on the importance of each node when forming links in each community, to describe the structure of link communities. We proceed to fit the model parameters by taking it as an optimization problem, and solve it using nonnegative matrix factorization. Thereafter, in order to automatically determine the number of communities, we extend the above method by introducing a strategy of iterative bipartition. This extended method not only finds the number of communities all by itself, but also obtains high efficiency, and thus it is more suitable to deal with large and unexplored real networks. We test this approach on both synthetic benchmarks and real-world networks including an application on a large biological network, and compare it with two highly related methods. Results demonstrate the superior performance of our approach over competing methods for the detection of link communities.

Abstract
This paper presents the first phase of the redevelopment of the Electric Vehicle Scenario Simulator (EVeSSi) tool. A new methodology to generate traffic demand scenarios for the Simulation of Urban MObility (SUMO) tool for urban traffic simulation is described. This methodology is based on a Portugal census database to generate a synthetic population for a given area under study. A realistic case study of a Portuguese city, Vila Real, is assessed. For this area the road network was created along with a synthetic population and public transport. The traffic results were obtained and an electric buses fleet was evaluated assuming that the actual fleet would be replaced in a near future. The energy requirements to charge the electric fleet overnight were estimated in order to evaluate the impacts that it would cause in the local electricity network.

Abstract
In this paper, the problem of optimal economic scheduling of multi-reservoir cascaded hydrothermal units is investigated in the presence of an individual pumped-storage generating unit in the network. The proposed problem is modeled in a multi-objective framework comprising two objective functions. The goal of the first objective function is to minimize the operation costs and the second one is set to minimize the emissions caused by the thermal units while all the technical constraints are satisfied. Furthermore, the valve loading effect is included in the first objective function as a sinusoidal function. The problem is modeled and solved as a Mixed Integer Non-Linear Programming (MINLP). The augmented É"-constraint technique and lexicographic optimization are employed to solve the problem. Numerical results obtained from implementing the model on a case study are discussed. Also, the decision making procedure has been done using a fuzzy satisfying method to select the most preferred solution among the Pareto solutions derived through solving the multi-objective problem.

Abstract
As e-learning gradually evolved many specialized and disparate systems appeared to fulfil the needs of teachers and students, such as repositories of learning objects, authoring tools, intelligent tutors and automatic evaluators. This heterogeneity raises interoperability issues giving the standardization of content an important role in e-learning. This article presents a survey on current e-learning content aggregation standards focusing on their internal organization and packaging. This study is part of an effort to choose the most suitable specifications and standards for an e-learning framework called Ensemble defined as a conceptual tool to organize a network of e-learning systems and services for domains with complex evaluation.

Abstract
In most transportation infrastructure projects, earthworks are generally associated with the highest percentage costs and durations. Because these tasks are reliant on heavy machinery and repetitive tasks, they are strongly susceptible to optimization. This paper presents a study based on the application of artificial neural networks (ANN) on data originated from an actual earthwork construction project. Results show a good adjustment to the data while emphasizing the importance of optimal equipment allocation throughout the construction site. Finally, the architecture of an intelligent earthwork optimization system is presented, combining both data mining (DM) and modern optimization technologies, among others, to support equipment distribution optimization in earthwork projects. © ASCE 2014.