Abstract
Imbalanced classification has been extensively researched in the last years due to its prevalence in real-world datasets, ranging from very different topics such as health care or fraud detection. This literature has long been dominated by variations of the same family of solutions (e.g. mainly resampling and cost-sensitive learning). Recently, a new and promising way of tackling this problem has been introduced: learning with scoring pairwise ranking so that each pair of classes contribute in tandem to the decision boundary. In this sense, the paper addresses the problem of class imbalance in the context of ordinal regression, proposing two novel contributions: (a) approaching the imbalance by binary pairwise ranking using a well-known label decomposition ensemble, and (b) introducing a regularization into this ensemble so that parallel decision boundaries are favored. These are two independent contributions that synergize well. Our model is tested using linear Support Vector Machines and our results are compared against state-of-the-art models. Both approaches show promising performance in ordinal class imbalance, with an overall 15% improvement relative to the state-of-the-art, as evaluated by a balanced metric.

Abstract
FLUX is a platform for dynamically reconfigurable crowd-sensing using mobile devices like smartphones and tablets, programmed under a notion of region-based sensing. Each region is defined by a set of physical constraints that determine the sensing scope, e.g., based on device position or other environmental variables, plus a set of periodic tasks that perform the actual sensing. The resulting behavior is inherently dynamic: as a device's state changes, e.g., moves in space, it enters and/or leaves different regions, thereby changing the set of active tasks; moreover, regions can be added, deleted, and reprogrammed on-the-fly. FLUX makes use of a domain-specific language for sensing tasks that is compiled into abstract bytecode, later executed by a low-footprint virtual machine within a device, guaranteeing runtime safety by construction. For region/task dissemination, FLUX employs a broker that holds a changeable region configuration plus gateways that mirror the configuration throughout different network access points to which devices connect. Sensing data is streamed by devices to gateways and then back to the broker. Live or archived data streams are in turn fed by the broker to data-processing clients, which interface with the broker using a publish/subscribe API. We conducted two case-study experiments illustrating FLUX: a single-region deployment to monitor WiFi signal quality, and a multi-region deployment to monitor noise, temperature, and places-of-interest based on device movement.

Abstract
Considering an environment that consists of several services, applications and platforms, each present entity produces a certain amount of data. With so many sources of data, there are a number of things bound to exist: different formats of information, redundancy and no consistent standards of information. In environments as these, the collaboration between different entities creates an opportunity for innovation, where data interoperability allows for the re-use of information, the possibility of different services taking advantage of other third-party sources and the development of new businesses from existing information. This, however, is only possible if there is some sort of interoperability between the data, a way for it to be transmitted from entity to entity, always with the possibility of creating value with its manipulation and consumption. This paper exposes the work done in the development of a platform focused on data, looking into its forms of representation and how to solve the problems caused by the ever existing necessity of data interoperability between systems. The possibility for maintaining and creating Open Data Ecosystems is also analysed in the scope of the proposed platform. © 2018 IEEE.

Abstract

Abstract
The provision of gerontological care in rural areas represents an increased management challenge. Framed by the Portuguese Northeast reality, this paper reflects on the role and potential of pervasive and mobile computing in the management of gerontological care, specially in rural areas, explaining the potential of fusion between gerontology and technology and presents a conceptual model to frame it. Finally, it presents a software prototype developed for Android smartphones, capable of assist a gerontological care provider in some of their operational practices.

Abstract
UML Sequence Diagrams are used in different domains for specifying the required behaviour of software-based systems. However, the created diagrams are often used only as documentation, and not as a basis for generating subsequent lifecycle artifacts or for automated analysis. Several authors have proposed the transformation of Sequence Diagrams to executable Coloured Petri Nets (CPN), for simulation and testing purposes, but the transformations are not automated or are implemented in an ad-hoc way. To overcome those limitations, we present in this paper an approach to automatically translate Sequence Diagrams to CPN ready for execution with CPN Tools, taking advantage of model-to-model transformation techniques provided by the Eclipse Modelling Framework (EMF). The transformation rules are implemented in the Epsilon Transformation Language. We use the standard UML metamodel provided by EMF and the CPN metamodel provided by CPN Tools, so any Sequence Diagram created with an EMF compliant modelling tool can be transformed. An application example is presented to better illustrate the approach. Copyright