Abstract
Data stream is a challenging research topic in which data can continuously arrive with a probability distribution that may change over time. Depending on the changes in the data distribution, different phenomena can occur, for example, a concept drift. A concept drift occurs when the concepts associated with a dataset change when new data arrive. This paper proposes a new method based on k-Nearest Neighbors that implements a sliding window requiring less instances stored for training than existing methods. For such, a clustering approach is used to summarize data by placing labeled instances considered similar in the same cluster. Besides, instances close to the uncertainty border of existing classes are also stored, in a sliding window, to adapt the model to concept drift. The proposed method is experimentally compared with state-of-the-art classifiers from the data stream literature, regarding accuracy and processing time. According to the experimental results, the proposed method has better accuracy and less time consumption when fewer information about the concepts are stored in a single sliding window. © 2018, Springer Nature Switzerland AG.

Abstract
Maintaining the quality control of scientific literature is one of the main characteristics of the peer review process. However, it depends on the peers' effectiveness in minimizing the intrinsic subjectivity to the process. Publishers try to achieve this through training and guides for reviewers. However, there is no consensus as to what the main criteria for a good review are, which results in poorly reasoned or vague reports that do not assist the editor in his decision nor the author in improvement of research. This project proposes a quality model for reviewing articles and a framework for their automatic classification through machine learning techniques. This proposal will be useful for: i) reviewers as a guideline for the preparation of the review report, editors as an indicator of the quality of the received revisions, and the authors as a model for self-evaluation of their research.

Abstract
This paper describes a two-month summer intensive course designed to introduce participants with a hands-on technical craft on robotics and to acquire experience in the low-level details of embedded systems. Attendants started this course with a brief introduction to robotics; learned to draw, design and create a personalized 3D structure for their mobile robotic platform and developed skills in embedded systems. They were familiarize with the practices used in robotics, learning to connect all sensors and actuator, developing a typical application on differential kinematic using Arduino, exploring ROS features under Raspberry Pi environment and Arduino - Raspberry Pi communication. Different paradigms and some real applications and programming were addressed on the topic of Artificial Intelligence. This paper describes not just the concept, layout and methodology used on RobotCraft 2017 but also presents the participants knowledge background and their overall opinions, leading to focus on lessons learned and suggestions for future editions.

Abstract
Previous findings have shown that users with intellectual disabilities navigate the Web more easily and with greater interest when images are used to represent hyperlinks (Rocha, 2008; 2014; Rocha et al., 2012). Although images can be better for navigation purposes, there is a need to understand how these images should be designed to enhance users' interaction with digital content for this particular group of people. The purpose of this study is to measure the user's preference for image-type representation (Object, Action and Universe), within four different categories (music, movies, sports, games). The sample consisted of 20 individuals with intellectual disabilities, their ages ranging from 22 to 49 years old. The results showed that they preferred less complex images, namely object representative images, and that categories had no effect.

Abstract
Current real-time embedded systems development frameworks lack support for the verification of properties using explicit time where counting time (i.e., durations) may play an important role in the development process. Focusing on the real-time constraints inherent to these systems, we present a framework that addresses the specification of duration properties for runtime verification by employing a fragment of metric temporal logic with durations. We also provide an overview of the framework, the synthesis tools, and the library to support monitoring properties for real-time systems developed in C++11. The results obtained provide clear evidence of the feasibility and advantages of employing a duration-sensitive formalism to increase the dependability of avionic controller systems such as the PX4 and the Ardupilot flight stacks.

Abstract
The scalable analysis of crowdsourced data repositories and streams has quickly become a critical experimental asset in multiple fields. It enables the systematic aggregation of otherwise disperse data sources and their efficient processing using significant amounts of computational resources. However, the considerable amount of crowdsourced social data and the numerous criteria to observe can limit analytical off-line and on-line processing due to the intrinsic computational complexity. This paper demonstrates the efficient parallelisation of profiling and recommendation algorithms using tourism crowdsourced data repositories and streams. Using the Yelp data set for restaurants, we have explored two different profiling approaches: entity-based and feature-based using ratings, comments, and location. Concerning recommendation, we use a collaborative recommendation filter employing singular value decomposition with stochastic gradient descent (SVD-SGD). To accurately compute the final recommendations, we have applied post-recommendation filters based on venue suitability, value for money, and sentiment. Additionally, we have built a social graph for enrichment. Our master-worker implementation shows super-linear scalability for 10, 20, 30, 40, 50, and 60 concurrent instances.