Abstract
Energy consumption is a major problem in Wireless Sensor Networks (WSNs) since in most scenarios nodes operate on batteries with reduced size and low capacity. Battery lifetime estimation is therefore crucial to evaluate the network behavior over time. In addition, battery lifetime estimation is a major challenge since it depends on many factors, such as the duty cycle of supported applications and the intrinsic chemical reactions within the batteries. Several battery models have been proposed to deal with battery lifetime estimation. However, most of the available models address only high-capacity batteries. This paper discusses a method for extracting the parameters for the Kinetic Battery Model (KiBaM) used within WSN context. The target is to define a battery model that helps to estimate the lifetime of nodes using low-capacity batteries and which operate in a duty cycle scheme. Results show that KiBaM is an adequate model for WSNs since it presents low error when compared with experimental results using real batteries.

Abstract
Many real-world data-mining applications involve obtaining predictive models using datasets with strongly imbalanced distributions of the target variable. Frequently, the least-common values of this target variable are associated with events that are highly relevant for end users (e.g., fraud detection, unusual returns on stock markets, anticipation of catastrophes, etc.). Moreover, the events may have different costs and benefits, which, when associated with the rarity of some of them on the available training data, creates serious problems to predictive modeling techniques. This article presents a survey of existing techniques for handling these important applications of predictive analytics. Although most of the existing work addresses classification tasks (nominal target variables), we also describe methods designed to handle similar problems within regression tasks (numeric target variables). In this survey, we discuss the main challenges raised by imbalanced domains, propose a definition of the problem, describe the main approaches to these tasks, propose a taxonomy of the methods, summarize the conclusions of existing comparative studies as well as some theoretical analyses of some methods, and refer to some related problems within predictive modeling.

Abstract
The electrification of the transportation sector is likely to contribute reducing the global dependency on oil and is expected to drive investments to renewable and intermittent energy sources, by taking advantage of it energy storage capacity. In order to facilitate the EV integration to the grid, and to take advantage of the battery storage and the Vehicle-to-Grid (V2G) scheme, smart charging strategies will be required. However, these strategies rarely consider all relevant costs, such as battery degradation. This work analyses the profitability of bidirectional energy transfer, i.e. the possibility of using aggregated EV batteries as storage for energy which can be injected back to the grid, by considering battery degradation as a cost included in the proposed strategy. A mixed integer linear problem (MILP) for minimizing energy costs and battery ageing costs for EV owners is formulated. The battery degradation due to charging and discharging in the V2G scheme is accounted for in the model used. Two case studies of overnight charging of EVs in Sweden and in Spain are proposed. Results show that given current energy prices and battery costs, V2G is not profitable for EV owners, but if battery prices decrease as expected, the V2G will be present in the medium term. © 2016 IEEE.

Abstract
Recommender systems arose in response to the excess of available online information. These systems assign, to a given individual, suggestions of items that may be relevant. These system's monitoring and evaluation are fundamental to the proper functioning of many business related services. It is the goal of this paper to create a tool capable of collecting, aggregating and supervising the results obtained from the recommendation systems' evaluation. To achieve this goal, a multi-granularity approach is developed and implemented in order to organize the different levels of the problem. This tool also aims to tackle the lack of mechanisms to enable visually assessment of the performance of a recommender systems' algorithm. A functional prototype of the application is presented, with the purpose of validating the solution's concept.

Abstract

Abstract
By designing systems as sets of modules that can be composed into larger applications, developers unleash a multitude of advantages. The promise of AOP (Aspect-Oriented Programming) is to enable developers to organize crosscutting concerns into separate units of modularity making it easier to accomplish this vision. However, AOP does not allow unit tests to be untangled, which impairs the development of properly tested independent modules. This paper presents a technique that enables developers to encapsulate crosscutting concerns using AOP and still be able to develop reusable unit tests. Our approach uses incremental testing and invasive aspects to modify and adapt tests. The approach was evaluated in a medium scale project with promising results. Without using the proposed technique, due to the presence of invasive aspects, some unit tests would have to be discarded or modified to accommodate the changes made by them. This would have a profound impact on the overall modularity and, in particular, on the reusability of those modules. We will show that this technique enables proper unit tests that can be reused even when coupled with aspect-oriented code.