Abstract
Identifying the best machine learning algorithm for a given problem continues to be an active area of research. In this paper we present a new method which exploits both meta-level information acquired in past experiments and active testing, an algorithm selection strategy. Active testing attempts to iteratively identify an algorithm whose performance will most likely exceed the performance of previously tried algorithms. The novel method described in this paper uses tests on smaller data sample to rank the most promising candidates, thus optimizing the schedule of experiments to be carried out. The experimental results show that this approach leads to considerably faster algorithm selection.

Abstract
The three-phase state estimation algorithms developed for distribution systems (DS) are based on traditional approaches, requiring components modeling and the complete knowledge of grid parameters. These algorithms are capable of dealing with the particular characteristics of DS but cannot be used in cases where grid topology and parameters are unknown, which is the most common situation in existing low voltage grids. This paper presents a novel three-phase state estimator for DS that enables the explicit estimation of voltage magnitudes and phase angles in all phases, neutral, and ground wires even when grid topology and parameters are unknown. The proposed approach is based on the use of auto-associative neural networks, the autoencoders (AE), which only require an historical database and few quasi-real-time measurements to perform an effective state estimation. Two test cases were used to evaluate the algorithm's performance: a low and a medium voltage grid. Results show that the algorithm provides accurate results even without information about grid topology and parameters. Several tests were performed to evaluate the best AE configuration. It was found that training an AE for each network feeder leads generally to better results than having a single AE for the entire system. The same happened when different AE were trained for each network phase in comparison with a single AE for the three phases.

Abstract
In this study, a multimodal interferometer based on a suspended core photonic crystal fiber (PCF) for simultaneous strain and temperature measurements is proposed. The structure is also employed for angle measurements. The sensor comprises a 3-mm-suspended core PCF between SMFs and is based on the combination of two multimodal interferences with different frequency fringe patterns. The interferometric patterns show different sensitivity responses to strain and temperature. Through a low-pass frequency filtering of the detected spectrum, the wavelength shift of the two patterns can be measured allowing the discrimination of strain and temperature effects with resolutions of 0.45 degrees C and 4.02 mu epsilon, respectively. The sensor is also characterized for angle measurements showing a maximum sensitivity of 9.17 pm/degrees in the range from 0 degrees to 90 degrees. It is demonstrated that with this sensing structure is possible to obtain simultaneous measurement of bend angle and temperature with resolutions of 1.69 degrees and 0.92 degrees C, respectively.

Abstract
These tutorial notes present a methodology for spreadsheet engineering. First, we present data mining and database techniques to reason about spreadsheet data. These techniques are used to compute relationships between spreadsheet elements (cells/columns/rows), which are later used to infer a model defining the business logic of the spreadsheet. Such a model of a spreadsheet data is a visual domain specific language that we embed in a well-known spreadsheet system. The embedded model is the building block to define techniques for model-driven spreadsheet development, where advanced techniques are used to guarantee the model-instance synchronization. In this model-driven environment, any user data update has to follow the model-instance conformance relation, thus, guiding spreadsheet users to introduce correct data. Data refinement techniques are used to synchronize models and instances after users update/evolve the model. These notes briefly describe our model-driven spreadsheet environment, the MDSheet environment, that implements the presented methodology. To evaluate both proposed techniques and the MDSheet tool, we have conducted, in laboratory sessions, an empirical study with the summer school participants. The results of this study are presented in these notes.

Abstract
It is increasingly important to assess and ensure the correct behaviour of mobile applications as their importance in everyday life keeps increasing. This paper presents an automatic testing approach combining reverse engineering with testing. The algorithm tries to identify existing User Interface (UI) patterns on a mobile application under test through a reverse engineering process and then tests them using generic test strategies called Test Patterns. The overall testing approach was implemented in the iMPAcT (Mobile PAttern Testing) tool and is illustrated in a case study performed over some mobile applications as a proof-of-concept.

Abstract