Abstract
We propose a systematic concatenated coding scheme based on the combination of interleaving with powerful channel codes and jamming for wireless secrecy under the practical assumption of codes in the finite blocklength regime. The basic idea lies in generating a short random key that is used to shuffle/interleave information at the source, Alice. This key is then sent to the legitimate receiver, Bob, during a brief period of advantageous communication over the eavesdropper Eve (e.g., due to more interference from a jammer). Finally, the key is decoded at Bob to properly deinterleave the original information. Bob receives a better quality version of the interleaving key, therefore having the needed advantage over Eve. Information reliability is provided by a strong inner code, while security against Eve results from the proper selection of the outer code and interference levels over the key. We propose a methodology for selection of the outer code with reliability and security constraints. For that, we introduce bit error complementary cumulative distribution function metrics, suitable for security and reliability analysis of error correcting codes.

Abstract
Social media has become one of the most prolific felds for interchange of multidisciplinary expertise. In this paper, computer science, communication and management are brought together for the development of a sound strategic content analysis, in the Higher Education Sector. The authors present a study comprised of two stages: analysis of SM content and corresponding audience engagement according to a weighted scale, and a classification of content strategies, which builds on different noticeable articulations of editorial areas among organizations. Their approach is based on an automatic classification of content according to a predefned editorial model. The proposed methodology and research results offer academic and practical fndings for organizations striving on social media. Copyright © 2015,.

Abstract
The adoption of social media networks by organizations has been increasing, mainly by using more social networks but also by constantly increasing on the number of messages and received comments posted on these channels. Interestingly, this process apparently has not been accompanied by a carefully planned and strategically design process to provide the essential alignment with organizational goals. This study is framed in the tertiary sector, the Higher Education Sector (HES), which despite its peculiarities, is no exception to the above limitations, and is facing an increased competitive environment. In this paper we present a sector benchmarking process, and the respective analysis, to provide insights on the sector's tendency, as well as a threefold classification of the sector's social media strategies being pursued. The analysis builds upon a regulatory communication framework and respective editorial model. We describe the results of our automatic text-mining and categorization information system, specifically developed to address and analyze the seven categories of HES' social media messages. Our results show that social media strategies have been focusing essentially on mediatization and building/maintaining the organizational image/reputation as well as on advertising educational services, but completely neglecting the dialogical dimension intrinsically linked to social media environments. (C) 2015 The Authors. Published by Elsevier B.V.

Abstract
Group work is an essential activity during both graduate and undergraduate formation. Although there is a vast theoretical literature and numerous case studies about group work, we haven’t yet seen much development concerning the assessment of individual group participants. The problem relies on the difficulty to have the perception of each student’s contribution towards the whole work. We propose and describe a novel tool to manage and assess individual group. Using the collected interactions from the tool usage we create a model for predicting ill-conditioned interactions which generate alerts. We also describe a functionality to predict the final activity grading, based on the interaction patterns and on an automatic classification of these interactions. © Springer International Publishing Switzerland 2015.

Abstract
Group work is an essential activity during both graduate and undergraduate formation. During group work Students develop a set of skills, and employ criticism which helps them to better handle future interpersonal situations. Although there is a vast theoretical literature and numerous case studies about group work, we haven't yet seen much development concerning the assessment of individual group participants. The problem relies on the difficulty to have the perception of each student contribution to the whole work. Nevertheless, more than frequently, the assessment of the group is transposed to each group participant, which in turn results in each student having the same final mark. We propose and describe a novel tool to manage and assess individual group work taking into account the amount of work, interaction, quality, and the temporal evolution of each group participant. The module features the possibility to predict the final activity grading, based on the interaction patterns and automatic comparison with former interaction patterns. We describe the conceptual design of our tool and present its two operating modes of the module. We then describe the methodology for the assessment in the two operating modes and how the tool collects data from interactions to predict final grading.

Abstract
The ultimate goal of feature selection is to select the smallest subset of features that yields minimum generalization error from an original set of features. This effectively reduces the feature space, and thus the complexity of classifiers. Though several algorithms have been proposed, no single one outperforms all the other in all scenarios, and the problem is still an actively researched field. This paper proposes a new hybrid parallel approach to perform feature selection. The idea is to use a filter metric to reduce feature space, and then use an innovative wrapper method to search extensively for the best solution. The proposed strategy is implemented on a shared memory parallel environment to speedup the process. We evaluated its parallel performance using up to 32 cores and our results show 30 times gain in speed. To test the performance of feature selection we used five datasets from the well known NIPS challenge and were able to obtain an average score of 95.90% for all solutions.