Abstract
Software Defined Networks (SDNs) provides a separation between the control plane and the forwarding plane of networks. The software implementation of the control plane and the built in data collection mechanisms of the OpenFlow protocol promise to be excellent tools to implement Machine Learning (ML) network control applications. A first step in that direction is to understand the type of data that can be collected in SDNs and how information can be learned from that data. In this work we describe a simple architecture deployed in an enterprise network that gathers traffic data using the OpenFlow protocol. We present the data-sets that can be obtained and show how several ML techniques can be applied to it for traffic classification. The results indicate that high accuracy classification can be obtained with the data-sets using supervised learning.

Abstract
The lack of mechanisms made to check reliability of information on social networks is evidenced by the spread of misinformation and rumors in this kind of system. Given this scenario, the provision of tools to assist users with information auditability on social networks needs an emergencial approach. This article describes a catalog, followed by a guide of features that provide auditability on social networks. The guide contains guidelines for development of funcionalities in order to promote the adoption of auditability and enable users to identify false information and validate content on social networks. © 2016 for this paper by its authors.

Abstract
Digital accessibility is recognized as a fundamental tool for an egalitarian society. Nevertheless, software accessibility is an under addressed topic in the discipline of software engineering and the academy in general. As a result, its development and implementation is compromised. This problem is depicted here with the help of some experiments that shows the poor attention which is dedicated to this topic. Some hypotheses that try to explain this problem are formulated, and some possible solutions are debated. As a conclusion, some insights are given and a new possible researched avenue is presented.

Abstract
Conflict-free Replicated Data Types (CRDTs) can simplify the design of deterministic eventual consistency. Considering the several CRDTs that have been deployed in production systems, counters are among the first. Counters are apparently simple, with a straightforward inc/dec/read API, but can require complex implementations and several variants have been specified and coded. Unlike sets and registers, that can be adapted to operate inside maps, current counter approaches exhibit anomalies when embedded in maps. Here, we illustrate the anomaly and propose a solution, based on a new counter model and implementation.

Abstract

Abstract
Smartphones are everywhere, and they are a very attractive platform to perform unobtrusive monitoring of users. In this work, we use common features of modern smartphones to build a human activity recognition (HAR) system for elderly care. We have built a classifier that detects the transport mode of the user including whether an individual is inactive, walking, in bus, in car, in train or in metro. We evaluated our approach using over 24 hours of transportation data from a group of 15 individuals. Our tests show that our classifier can detect the transportation mode with over 90% accuracy.