Abstract
Demand-side solutions are one of the most important customer-dependent options among innovative smart grid technologies. Flexible loads can be controlled and coordinated in several ways to operate in favor of the grid. Contrary to conventional participators in grid services, responding to grid requests is not the primary objective of the owners of demand-side resources. Therefore, it is a vital task for demand side service operators to provide maximized and reliable participation. However, motivation factors may vary due to demographic characteristics of the society and there are important diversities due to cultural differences of countries. This study investigates consumer expectations, preferences and concerns on demand response (DR) and deployable gamification techniques in Turkey. A detailed survey is conducted with individuals and results are analyzed to evaluate general trends together with distinctive customer patterns.

Abstract
This paper presents a teaching/learning experiment running in the laboratorial curricular unit Project I of the 4th year of the Integrated Master in Industrial Electronics and Computers Engineering at the University of Minho. Project specifications were defined by the three teachers involved in the experience and students were encouraged to look on different solutions for a real-word problem. In a concurrent way, students designed, developed and implemented didactic rigs to control a gantry crane system. The control was performed in open-loop, based on the Posicast feedforward technique, and in closed-loop, using a two-degrees of freedom configuration. The experiment procedure and the project outcomes of two solutions proposed by the students are presented.

Abstract
In classification problems, a dataset is said to be imbalanced when the distribution of the target variable is very unequal. Classes contribute unequally to the decision boundary, and special metrics are used to evaluate these datasets. In previous work, we presented pairwise ranking as a method for binary imbalanced classification, and extended to the ordinal case using weights. In this work, we extend ordinal classification using traditional balancing methods. A comparison is made against traditional and ordinal SVMs, in which the ranking adaption proposed is found to be competitive.

Abstract
The exponential growth of data produced, the ever faster and ubiquitous connectivity, and the collaborative processing tools lead to a clear shift of data stores from local servers to the cloud. This migration occurring across different application domains and types of users|individual or corporate|raises two immediate challenges. First, outsourcing data introduces security risks, hence protection mechanisms must be put in place to provide guarantees such as privacy, confidentiality and integrity. Second, there is no \one-size-fits-all" solution that would provide the right level of safety or performance for all applications and users, and it is therefore necessary to provide mechanisms that can be tailored to the various deployment scenarios. In this paper, we address both challenges by introducing SafeFS, a modular architecture based on software-defined storage principles featuring stackable building blocks that can be combined to construct a secure distributed file system. SafeFS allows users to specialize their data store to their specific needs by choosing the combination of blocks that provide the best safety and performance tradeoffs. The file system is implemented in user space using FUSE and can access remote data stores. The provided building blocks notably include mechanisms based on encryption, replication, and coding. We implemented SafeFS and performed indepth evaluation across a range of workloads. Results reveal that while each layer has a cost, one can build safe yet efficient storage architectures. Furthermore, the different combinations of blocks sometimes yield surprising tradeoffs. © 2017 ACM.

Abstract
In this paper we present the INESC Key Detection (IKD) system which incorporates a novel method for dynamically biasing key mode estimation using the spatial displacement of beat-synchronous Tonal Interval Vectors (TIVs). We evaluate the performance of the IKD system at finding the global key on three annotated audio datasets and using three key-defining profiles. Results demonstrate the effectiveness of the mode bias in favoring either the major or minor mode, thus allowing users to fine tune this variable to improve correct key estimates on style-specific music datasets or to balance predictions across key modes on unknown input sources.

Abstract
A Fabry-Perot air bubble microcavity fabricated between a section of single mode fiber and a multimode fiber is proposed. The study of the microcavities growth with the number of applied arcs is performed. The sensors are tested for lateral load and strain, where sensitivities of 0.32 nm/N and 2.11 nm/N and of 4.49 pm/mu epsilon and 9.12 pm/mu epsilon are obtained for the 47 mu m and 161 mu m long cavities, respectively. The way of manufacturing using a standard fusion splicer and given that no oils or etching solutions are involved, emerges as an alternative to the previously developed air bubble based sensors.