Abstract
Autonomous Underwater Vehicles (AUVs) are widely used as a cost-effective mean to carry out underwater missions. During long-term missions, AUVs may collect large amounts of data that usually needs to be sent to shore. An AUV may have to travel several kilometers before reaching an area of interest near the seafloor, thus surfacing is unpractical for most cases. Long-range underwater communications rely mostly on acoustic communications, which are characterized by very low bitrates, thus making the transfer of large amounts of data too slow. GROW is a novel solution for long-range, high bitrate underwater wireless communications between a survey unit (e.g., deep sea lander, AUV) and a central station at surface. GROW combines AUVs as data mules, short-range high bitrate wireless RF or optical communications, and long-range low bitrate acoustic communications for control. In this paper we present the Underwater Data Muling Protocol (UDMP), a communications protocol that enables the control and the scheduling of the Data Mule Units within the GROW framework. Experimental results obtained using an underwater testbed show that the use of UDMP and data mules can outperform acoustic communications, achieving equivalent throughput up to 150 times higher within the typical range of operation of the latter.

Abstract

Abstract
Detailed documentation and software tests are key factors for the success of a web application programming interface (API). When designing an API, especially in a design first approach, it is relevant to define a formal contract, known as API specification. This document must contain all necessary information regarding the API behavior. Thereby, the specification can be used to dynamically generate API components like documentation, client and server code, and software tests, reducing development and maintenance costs. This chapter presents a study of OpenAPI specification and its application on designing a new RESTful API for E-goi. It also presents a set of solutions for generating documentation, client code libraries, and test cases.

Abstract
Imbalanced domains are an important problem frequently arising in real world predictive analytics. A significant body of research has addressed imbalanced distributions in classification tasks, where the target variable is nominal. In the context of regression tasks, where the target variable is continuous, imbalanced distributions of the target variable also raise several challenges to learning algorithms. Imbalanced domains are characterized by: (1) a higher relevance being assigned to the performance on a subset of the target variable values; and (2) these most relevant values being underrepresented on the available data set. Recently, some proposals were made to address the problem of imbalanced distributions in regression. Still, this remains a scarcely explored issue with few existing solutions. This paper describes three new approaches for tackling the problem of imbalanced distributions in regression tasks. We propose the adaptation to regression tasks of random over-sampling and introduction of Gaussian Noise, and we present a new method called WEighted Relevance-based Combination Strategy (WERCS). An extensive set of experiments provides empirical evidence of the advantage of using the proposed strategies and, in particular, the WERCS method. We analyze the impact of different data characteristics in the performance of the methods. A data repository with 15 imbalanced regression data sets is also provided to the research community.

Abstract
The search for logistics best-practices in international trade has led to the appearance of the Berth Allocation Problem. If the vessels have release dates, the problem is proved to be NP-hard and the performance of exact algorithms is not satisfactory, leading to the use of metaheuristics. This paper develops a Hybrid Evolutionary Algorithm for the discrete and dynamic Berth Allocation Problem. A challenge of using Genetic Algorithms is the identification of the best approach to model a specific problem. This paper proposes the use of frontier techniques (Data Envelopment Analysis and Free Disposal Hull models) to compare the performances of alternative specifications of the parameters for the algorithm proposed and to identify efficient solutions.

Abstract
Smartphones are becoming more important in our everyday lives and it is increasingly common to perform critical tasks on these devices, such as making payments. For this reason, ensuring the quality of these applications is an important task. One way to do this is through software testing. However, the testing of these applications presents major challenges due to the wide variety of devices available in the market. In this context, automated testing gains more relevance. There are dynamic test approaches for testing mobile applications, but there are some challenges that need to be overcome for good results, such as, being able to explore the complete behaviour of the application (e.g., overcoming blocking points); choosing appropriate input data; testing dynamic behaviour; testing specific characteristics of mobile applications, such as specific forms of interaction, e.g., long press, and so on. This paper presents a dynamic exploration approach of Android mobile applications that aims to overcome some of the problems identified. During the exploration process, the algorithm builds a Finite State Machine where states are traversed screens and transitions between states describe events that allow moving from one screen to another. This approach is implemented as an extension of the iMPAcT tool. The approach is validated over real Google Play apps and the test coverage results achieved are presented, compared and discussed.