Abstract

Abstract
Many text classification problems in social networks, and other contexts, are also dynamic problems, where concepts drift through time, and meaningful labels are dynamic. In Twitter-based applications in particular, ensembles are often applied to problems that fit this description, for example sentiment analysis or adapting to drifting circumstances. While it can be straightforward to request different classifiers' input on such ensembles, our goal is to boost dynamic ensembles by combining performance metrics as efficiently as possible. We present a twofold performance-based framework to classify incoming tweets based on recent tweets. On the one hand, individual ensemble classifiers' performance is paramount in defining their contribution to the ensemble. On the other hand, examples are actively selected based on their ability to effectively contribute to the performance in classifying drifting concepts. The main step of the algorithm uses different performance metrics to determine both each classifier strength in the ensemble and each example importance, and hence lifetime, in the learning process. We demonstrate, on a drifted benchmark dataset, that our framework drives the classification performance considerably up for it to make a difference in a variety of applications.

Abstract
The lung cancer diagnosis is based on the search of lung nodules. Besides its characterization, it is also common to register the anatomical position of these findings. Even though computed-aided diagnosis systems tend to help in these tasks, there is still lacking a complete system that can qualitatively label the nodules in lung regions. In this way, this paper proposes an automatic lung reference model to facilitate the report of nodules between computed-aided diagnosis systems and the radiologist, and among radiologists. The model was applied to 115 computed tomography scans with manually and automatically segmented lobes, and the obtained sectors' variability was evaluated. As the sectors average variability within lobes is less or equal to 0.14, the model can be a good way to promote the report of lung nodules.

Abstract
In future wireless communication systems, several radio access technologies will coexist and interwork to provide a great variety of services with different requirements. Thus, the design of flexible and reconfigurable hardware is a relevant topic in wireless communications. The combination of high performance, programmability and flexibility makes Field-programmable gate array a convenient platform to design such systems, especially for base stations. This paper describes a dynamically reconfigurable baseband modulator for Orthogonal Frequency Division Multiplexing and Filter-bank Multicarrier modulation waveforms implemented on a Virtex-7 board. The design features Dynamic Partial Reconfiguration (DPR) capabilities to adapt its mode of operation at run-time and is compared with a functionally equivalent static multi-mode design regarding processing throughput, resource utilization, functional density and power consumption. The DPR-based design implementation reserves about half the resources used by static multi-mode counterpart. Consequently, the baseband processing dynamic power consumption observed in the DPR-based design is between 26 mW to 90 mW lower than in the static multi-mode design, representing a dynamic power reduction between 13% to 52%. The worst-case DPR latency measured was 1.051 ms, while the DPR energy overhead is below 1.5 mJ. Considering latency requirements for modern wireless standards and power consumption constraints for commercial base stations, the DPR application is shown to be valuable in multi-standard and multi-mode systems, as well as in scenarios such as multiple-input and multiple-output or dynamic spectrum aggregation.

Abstract

Abstract
This paper describes a customized risk assessment framework to be applied in military shipbuilding projects. The framework incorporates the Delphi method with visual diagrams, Bayesian Networks (BN) and the expression of expert opinions through linguistic variables. Noisy-OR and Leak Canonical models are used to determine the conditional probabilities of the BN model. The approach can easily be adapted for other shipbuilding construction projects. The visual diagrams that support the Delphi questionnaire favor the comprehensive visualization of the interdependencies between risks, causes, risks and causes, and risks and effects. The applicability of the framework is illustrated through the assessment of risk of two real military shipbuilding projects. This assessment includes a sensitivity analysis that is useful to prioritize mitigation actions. In the two cases studies, the risks with higher probability of occurrence were failures or errors in production, of the contracted, in the requirements, and in planning. The results of the sensitivity analysis showed that a set of mitigation actions directed at relatively easily controllable causes would have achieved important reductions in risk probabilities.