Abstract
Statistical relational learning (SRL) constructs probabilistic models from relational databases. A key capability of SRL is the learning of arcs (in the Bayes net sense) connecting entries in different rows of a relational table, or in different tables. Nevertheless, SRL approaches currently are constrained to use the existing database schema. For many database applications, users find it profitable to define alternative "views" of the database, in effect defining new fields or tables. Such new fields or tables can also be highly useful in learning. We provide SRL with the capability of learning new views.

Abstract

Abstract
One of the main advantages of Logic Programming (LP) is that it provides an excellent framework for the parallel execution of programs. In this work we investigate novel techniques to efficiently exploit parallelism from real-world applications in low cost multi-core architectures. To achieve these goals, we revive and redesign the YapOr system to exploit or-parallelism based on a multi-threaded implementation. Our new approach takes full advantage of the state-of-the-art fast and optimized YAP Prolog engine and shares the underlying execution environment, scheduler and most of the data structures used to support YapOr's model. Initial experiments with our new approach consistently achieve almost linear speedups for most of the applications, proving itself as a good alternative for exploiting implicit parallelism in the currently available low cost multi-core architectures.

Abstract
In this work we perform a detailed study of different or-scheduling strategies varying several parameters in two or-parallel systems, YapOr and ThOr, running on multi-core machines. Our results show that some kinds of applications are sensitive to the choice of scheduling strategy adopted. In particular, the choice of scheduling parameters mostly affect applications that have short execution times, which, despite having speedups, have their performance significantly affected. Our results also show that topmost dispatching can be more advantageous than bottommost dispatching, a finding that contradicts previous works in this area. One last finding is that YapOr and ThOr are affected differently by changes in scheduling with ThOr performing significantly better than YapOr in several applications.

Abstract
Digital stethoscopes are medical devices that can collect, store and sometimes transmit acoustic auscultation signals in a digital format. These can then be replayed, sent to a colleague for a second opinion, studied in detail after an auscultation, used for training or, as we envision it, can be used as a cheap powerful tool for screening cardiac pathologies. In this work, we present the design, development and deployment of a prototype for collecting and annotating auscultation signals within real hospital environments. Our main objective is not only pave the way for future unobtrusive systems for cardiac pathology screening, but more immediately we aim to create a repository of annotated auscultation signals for biomedical signal processing and machine learning research. The presented prototype revolves around a digital stethoscope that can stream the collected audio signal to a nearby tablet PC. Interaction with this system is based on two models: a data collection model adequate for the uncontrolled hospital environments of both emergency room and primary care, and a data annotation model for offline metadata input. A specific data model was created for the repository. The prototype has been deployed and is currently being tested in two Hospitals, one in Portugal and one in Brazil.

Abstract
The DigiScope project aims at developing a digitally enhanced stethoscope capable of using state of the art technology in order to help physicians in their daily medical routine. One of the main tasks of DigiScope is to build a repository of auscultations (sound and medical related data). In this work, we present a preliminary analysis and study of the first auscultations performed on children of a Brazilian hospital. Results indicate that classifiers can be obtained that distinguish reasonably well patients with cardiac pathologies from those that do not have pathologies.