Abstract
Computer networking is a central topic in computer science courses curricula offered by higher education institutions. Network virtualization and simulation tools, like GNS3, allows students and practitioners to test real world networking configuration scenarios and to configure complex network scenarios by configuring virtualized equipments, such as routers and switches, through each one's virtual console. The configuration of advanced network topics in GNS3 requires that students have to apply basic and very repetitive IP configuration tasks in all network equipments. As the network topology grows, so does the amount of network equipments to be configured, which may lead to logical configuration errors. In this paper we propose an extension for GNS3 network virtualizer, to automatically generate a valid configuration of all the network equipments in a GNS3 scenario. Our implementation is able to automatically produce an initial IP and routing configuration of all the Cisco virtual equipments by using the GNS3 specification files. We tested this extension against a set of networked scenarios which proved the robustness, readiness and speedup of the overall configuration tasks. In a learning environment, this feature may save time for all networking practitioners, both beginners or advanced, who aim to configure and test network topologies, since it automatically produces a valid and operational configuration for all the equipments designed in a GNS3 environment.

Abstract
Social networks are making part of the daily routine of millions of users. Twitter is among Facebook and Instagram one of the most used, and can be seen as a relevant source of information as users share not only daily status, but rapidly propagate news and events that occur worldwide. Considering the dynamic nature of social networks, and their potential in information spread, it is imperative to find learning strategies able to learn in these environments and cope with their dynamic nature. Time plays an important role by easily out-dating information, being crucial to understand how informative can past events be to current learning models and for how long it is relevant to store previously seen information, to avoid the computation burden associated with the amount of data produced. In this paper we study the impact of longstanding messages in micro-blogging classification by using different training time-window sizes in the learning process. Since there are few studies dealing with drift in Twitter and thus little is known about the types of drift that may occur, we simulate different types of drift in an artificial dataset to evaluate and validate our strategy. Results shed light on the relevance of previously seen examples according to different types of drift.

Abstract
USB dongles have been used by a wide range of software manufacturers to store a copy-protected of their application's license. The licenses validation procedure through USB dongles faces several concerns, as the risks of theft or losing dongle. Also, in scenarios where the number of dongles is reduced, users may have to wait for dongle access, which may lead to loss of productivity. In this paper we propose a client/server distributed architecture for remote software licenses validation, through USB/IP protocol. The proposed approach aims to take advantage of USB/IP for distributed access to a set of USB dongles physically connected to a remote USB server, over a TCP/IP network. We describe the deployment and enhancements made to an existing open source USB/ IP implementation and also present the results obtained with this architecture in a real world scenario, for validation of computer forensics applications licenses that uses USB dongles.

Abstract
Social media advancements and the rapid increase in volume and complexity of data generated by Internet services are becoming challenging not only technologically, but also in terms of application areas. Performance and availability of data processing are critical factors that need to be evaluated since conventional data processing mechanisms may not provide adequate support. Apache Hadoop with Mahout is a framework to storage and process data at large-scale, including different tools to distribute processing. It has been considered an effective tool currently used by both small and large businesses and corporations, like Google and Facebook, but also public and private healthcare institutions. Given its recent emergence and the increasing complexity of the associated technological issues, a variety of holistic framework solutions have been put forward for each specific application. In this work, we propose a generic functional architecture with Apache Hadoop framework and Mahout for handling, storing and analyzing big data that can be used in different scenarios. To demonstrate its value, we will show its features, advantages and applications on health Twitter data. We show that big health social data can generate important information, valuable both for common users and practitioners. Preliminary results of data analysis on Twitter health data using Apache Hadoop demonstrate the potential of the combination of these technologies. (C) 2015 The Authors. Published by Elsevier B.V.

Abstract
Machine learning approaches often focus on optimizing the algorithm rather than assuring that the source data is as rich as possible. However, when it is possible to enhance the input examples to construct models, one should consider it thoroughly. In this work, we propose a technique to define the best set of training examples using dynamic ensembles in text classification scenarios. In dynamic environments, where new data is constantly appearing, old data is usually disregarded, but sometimes some of those disregarded examples may carry substantial information. We propose a method that determines the most relevant examples by analysing their behaviour when defining separating planes or thresholds between classes. Those examples, deemed better than others, are kept for a longer time-window than the rest. Results on a Twitter scenario show that keeping those examples enhances the final classification performance.

Abstract
ICT in healthcare businesses has been growing in Portugal in the past few decades. The implementation of large scale information systems in hospitals, the deployment of electronic prescription and electronic patient records applications are just a few examples. Telemedicine is another emergent and widely used ICT solution to smooth the communication between patients and healthcare professionals, by allowing video and voice transfer over the Internet. Although there are several implementations of telemedicine solutions, they usually have some drawbacks, namely: i) too specific for a purpose; ii) based on proprietary applications; iii) require additional software installation; iv) and usually have associated costs. In this paper we propose a telemedicine solution based on WebRTC Application Programming Interface (API) to transmit video and voice in real time over the Internet, through a web browser. Besides microphone and webcam control, we have also included two additional functionalities that may be useful to both patients and healthcare professionals during the communication, namely i) bidirectional sending files capability and ii) shared whiteboard which allows free drawing. The proposed solution uses exclusively open source software components and requires solely a WebRTC compatible web browser, like Google Chrome or Firefox. We have made two types of tests in healthcare environment: i) a bidirectional patient-doctor communication; ii) and connecting at one end an external USB medical device with an integrated webcam. The results were promising, since they revealed the potential of using WebRTC API to control microphone and webcam in a telemedicine application, as well as the appropriateness and acceptance of the features included. (C) 2016 Published by Elsevier B.V.