Abstract
As organizations are entering social media, determining their current strategy will allow to combine monitoring and benchmarking methods to foster the identification of opportunities and threats, which can serve as inputs for the evaluation of social media strategies' and eventual readjustments, and a subsequent efficiency measurement. Higher Educational Institutions (HEI) are not different from other organizations in which concerns these problems. To address these challenges, we propose an automatic procedure to assess the posting behavior and strategy identification for each higher educational institution. We used a sample of the 10-top worldwide ranked educational institutions in this study and collected the posts from their official Facebook pages during an entire school year. Our study was conducted on the frequency and intensity of publications by universities, which included an analysis of the number of responses to 'posts' over time in the form of 'shares'. Finally, the content of the posts was analyzed according to the topics covered in the messages. This process allowed us to identify the editorial areas that each university uses the most and in which are more active. © 2018 The Authors. Published by Elsevier Ltd..

Abstract
Organizations are rushing into social media networks following a worldwide trend to create a social presence in multiple media channels. However, a social media strategy needs to be aligned with and framed in the overall organizational strategic management goals. Higher Educational Institutions (HEI) are not different from other organizations in which concerns these problems. Determining the organizational positioning of an organization current strategy will allow to combine monitoring and benchmarking methods to foster the identification of opportunities and threats, which can serve as inputs for the internal evaluation of social media strategies', for the necessary strategic readjustments and a subsequent efficiency measurement. In order to address these challenges, we propose a three-step automatic data-mining procedure to assess the posting behavior and strategy of HEI, understand the editorial policy behind it, and predict the future HEI engagement. We used a sample of the 5-top ranked educational institutions in 2017. We collected the posts from each HEI official Facebook page during an entire school year. Our method showed high degree of accuracy and is also capable of describing which topics are most common in each university's social media content strategy and relate them to the corresponding response from their publics.

Abstract
A few years back organizations were rushing into social media environments following the worldwide trend to create a social presence in multiple channels and / or to explore their potential. Currently, after having gone through a period of experimentation and consolidation of that presence, it is important to understand and to report on how the performance and communication efficiency of organizations has evolved. On previous studies, where we focused on the public higher education sector, we have identified a set of organizations that presented behaviour which was typical from yearly social media adopters, with very low relative performance and communication efficiency. Using data and text mining tools, and techniques, we showed that these organizations revealed very low frequency of publication of messages and very low engagement among their audiences. At the time, the analysis of this sector posed challenges to the confirmation of whether these content strategies were representative enough and if they were a result of an effective and permanent organizational behaviour on social media, or just a result of a stage of social media adoption. In this paper, we present a longitudinal study that portrays the evolution of the organizational behaviour of these organizations on social media, concerning their relative performance and their communication efficiency after a four-year period. Our analysis is based on how and if they have evolved from that stage by fine-tuning their social media communications. We also present findings concerning the content strategy structure evolution along the past four years, concerning the type of content used in higher education institutions' social media strategies, to obtain the best possible return on engagement from the publics (fans), demonstrating how these organizations have either dropped Facebook or optimized their type of content to foster higher return. Thus, on this longitudinal study we present and benchmark the current state of performance of public higher education institutions, concerning the path they undertook in the past four years.

Abstract

Abstract
Reducing the dimensionality of datasets is a fundamental step in the task of building a classification model. Feature selection is the process of selecting a smaller subset of features from the original one in order to enhance the performance of the classification model. The problem is known to be NP-hard, and despite the existence of several algorithms there is not one that outperforms the others in all scenarios. Due to the complexity of the problem usually feature selection algorithms have to compromise the quality of their solutions in order to execute in a practicable amount of time. Parallel computing techniques emerge as a potential solution to tackle this problem. There are several approaches that already execute feature selection in parallel resorting to synchronous models. These are preferred due to their simplicity and capability to use with any feature selection algorithm. However, synchronous models implement pausing points during the execution flow, which decrease the parallel performance. In this paper, we discuss the challenges of executing feature selection algorithms in parallel using asynchronous models, and present a feature selection algorithm that favours these models. Furthermore, we present two strategies for an asynchronous parallel execution not only of our algorithm but of any other feature selection approach. The first strategy solves the problem using the distributed memory paradigm, while the second exploits the use of shared memory. We evaluate the parallel performance of our strategies using up to 32 cores. The results show near linear speedups for both strategies, with the shared memory strategy outperforming the distributed one. Additionally, we provide an example of adapting our strategies to execute the Sequential forward Search asynchronously. We further test this version versus a synchronous one. Our results revealed that, by using an asynchronous strategy, we are able to save an average of 7.5% of the execution time.

Abstract
We describe a case study application for untethered video dissemination using a hybrid edge-cloud architecture featuring Android devices, possibly organised in WiFi-Direct groups, and Raspberry Pi-based cloudlets, structured in a mesh and also working as access points. The application was tested in the real-world scenario of a Portuguese volleyball league game. During the game, users of the application recorded videos and injected them in the edge-cloud. The cloudlet servers continuously synchronised their cached video contents over the mesh network, allowing users on different locations to share their videos, without resorting to any other network infrastructure. An analysis of the logs gathered during the experiment shows that such portable setups can easily disseminate videos to tens of users through the edge-cloud with low latencies. We observe that the edge-cloud may be naturally resilient to faulty cloudlets or devices, taking advantage of video caching within devices and WiFi-Direct groups, and of device churn to opportunistically disseminate videos.