Abstract
Misinformation propagation on social media has been significantly growing, reaching a major exposition in the 2016 United States Presidential Election. Since then, the scientific community and major tech companies have been working on the problem to avoid the propagation of misinformation. For this matter, research has been focused on three major sub-fields: the identification of fake news through the analysis of unreliable posts, the propagation patterns of posts in social media, and the detection of bots and spammers. However, few works have tried to identify the characteristics of a post that shares unreliable content and the associated behaviour of its account. This work presents four main contributions for this problem. First, we provide a methodology to build a large knowledge database with tweets who disseminate misinformation links. Then, we answer research questions on the data with the goal of bridging these problems to similar problem explored in the literature. Next, we focus on accounts which are constantly propagating misinformation links. Finally, based on the analysis conducted, we develop a model to detect social media accounts that spread unreliable content. Using Decision Trees, we achieved 96% in the F1-score metric, which provides reliability on our approach.

Abstract
Increasing demand for electricity, as well as rising environmental and economic concerns have resulted in renewable energy sources being a center of attraction. Integration of these renewable energy resources into power systems is usually achieved through distributed generation (DG) techniques, and the number of such applications increases daily. As conventional power systems do not have an infrastructure that is compatible with these energy sources and generation systems, such integration applications may cause various problems in power systems. Therefore, planning is an essential part of DG integration, especially for power systems with intermittent renewable energy sources with the objective of minimizing problems and maximizing benefits. In this study, a mathematical model is proposed to calculate the maximum permissible DG integration capacity without causing overvoltage problems in the power systems. In the proposed mathematical model, both the minimum loading condition and maximum generation condition are taken into consideration. In order to prove the effectiveness and the consistency of the proposed mathematical model, it is applied to a test system with different case studies, and the results are compared with the results obtained from other models in the literature.

Abstract

Abstract
We present MusikVerb, a novel digital reverberation capable of adapting its output to the harmonic context of a live music performance. The proposed reverberation is aware of the harmonic content of an audio input signal and ‘tunes’ the reverberation output to its harmonic content using a spectral filtering technique. The dynamic behavior of MusikVerb avoids the sonic clutter of traditional reverberation, and most importantly, fosters creative endeavor by providing new expressive and musically-aware uses of reverberation. Despite its applicability to any input audio signal, the proposed effect has been designed primarily as a guitar pedal effect and a standalone software application. Copyright

Abstract
The use of wireless sensor network nodes to support reliable communication exposes some challenging issues. For instance, the reduced available bandwidth combined with an error-prone communication medium impairs the provision of reliable communication services. Network coding techniques can be useful to mitigate some of these issues, where multiple message groups can be combined into single messages and retransmitted to their destinations, improving the network reliability and reducing the bandwidth consumption. However, an effective use of network coding requires the availability of wireless sensor network nodes able to encode/decode messages within the required timing constraints. This paper reports an experimental assessment of commercial off-the-shelf wireless sensor network nodes, running a set of network coding encoding/decoding tasks. The assessed nodes range from the high-performance ARM Cortex-M7 to the low capability Arduino Uno platforms, including some of the most popular ARM Cortex and ATMEL AVR processors. The performed experimental assessment demonstrates that highly complex network coding techniques (with fields as large as F28) can be efficiently implemented on a wide range of wireless sensor network nodes, including ARM Cortex, ATMEL AVR, and Arduino Uno platforms, smoothing some relevant reliable communication implementation issues.

Abstract
The study explores the technical optimization of an athlete through the use of intelligent system performance metrics that produce information obtained from inertial sensors associated to the coach's technical qualifications in real time, using Mixed Methods and Machine Learning. The purpose of this study is to illustrate, from the confusion matrices, the different performance metrics that provide information of high pertinence for the sports training in context. 2000 technical fencing actions with two levels of complexity were performed, captured through a single sensor applied in the armed hand and, simultaneously, the gesture’s qualification through a dichotomous way by the coach. The signals were divided into segments through Dynamic Time Warping, with the resulting extracted characteristics and qualitative assessments being fed to a Neural Network to learn the patterns inherent to a good or poor execution. The performance analysis of the resulting models returned a prediction accuracy of 76.6% and 72.7% for each exercise, but other metrics indicate the existence of high bias in the data. The study demonstrates the potential of intelligent algorithms to uncover trends not captured by other statistical methods. © Edições Desafio Singular.