Abstract
This paper considers the single machine scheduling problem with weighted quadratic tardiness costs. Three metaheuristics are presented, namely iterated local search, variable greedy and steady-state genetic algorithm procedures. These address a gap in the existing literature, which includes branch-and-bound algorithms (which can provide optimal solutions for small problems only) and dispatching rules (which are efficient and capable of providing adequate solutions for even quite large instances). A simple local search procedure which incorporates problem specific information is also proposed. The computational results show that the proposed metaheuristics clearly outperform the best of the existing procedures. Also, they provide an optimal solution for all (or nearly all, in the case of the variable greedy heuristic) the smaller size problems. The metaheuristics are quite close in what regards solution quality. Nevertheless, the iterated local search method provides the best solution, though at the expense of additional computational time. The exact opposite is true for the variable greedy procedure, while the genetic algorithm is a good all-around performer.

Abstract
This paper presents CloudAnchor, a brokerage platform conceived to help Small and Medium Sized Enterprises (SME) embrace Infrastructure as a Service (IaaS) cloud computing both as providers and consumers. The platform, which transacts automatically single and federated IaaS cloud resources, is a multi-layered Multi-Agent System (MAS) where providers, consumers and virtual providers, representing provider coalitions, are modelled by dedicated agents. Federated resources are detained and negotiated by virtual providers on behalf of the corresponding coalition of providers. CloudAnchor negotiates and establishes Service Level Agreements (SLA) on behalf of SME businesses regarding the provision of brokerage services as well as the provision of single and federated IaaS resources. The discovery, invitation, acceptance and negotiation processes rely on a distributed trust model designed to select the best business partners for consumers and providers and improve runtime.

Abstract
Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items - the subset of items co-rated by both users typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process - a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.

Abstract

Abstract
This descriptive study explores the state of CSR education and research in Portugal. It aims to depict the state of CSR in Portugal, in particular in what concerns the nature and the extent of education and research on CSR being undertaken in Portugal. The methodology used includes analysis of relevant literature and of business schools’ websites, and a survey by questionnaire among students at the business school at which the authors of this chapter teach and research. In terms of CSR practices, there is still some focus on social issues, given the state of development of the Portuguese economy. There are signs of CSR having reached a reasonable level of maturity. Although CSR education and research seem to be reasonably well developed in the leading business schools, the same is not the case with other schools. Research on CSR is concentrated in the schools in which CSR education is more developed.

Abstract
The efficiency of Public Transportation (PT) Networks is a major goal of any urban area authority. Advances on both location and communication devices drastically increased the availability of the data generated by their operations. Adequate Machine Learning methods can thus be applied to identify patterns useful to improve the Schedule Plan. In this paper, the authors propose a fully automated learning framework to determine the best Schedule Coverage to be assigned to a given PT network based on Automatic Vehicle location (AVL) and Automatic Passenger Counting (APC) data. We formulate this problem as a clustering one, where the best number of clusters is selected through an ad-hoc metric. This metric takes into account multiple domain constraints, computed using Sequence Mining and Probabilistic Reasoning. A case study from a large operator in Sweden was selected to validate our methodology. Experimental results suggest necessary changes on the Schedule coverage. Moreover, an impact study was conducted through a large-scale simulation over the affected time period. Its results uncovered potential improvements of the schedule reliability on a large scale. © Springer International Publishing Switzerland 2016.