A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease. Medical personnel should be contacted immediately in order to intervene in time before an acute state is reached, ensuring patient safety. This paper proposes an approach to an ambient intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure (CHF). Its novelty is the integration of: (i) personalized monitoring of the patients health status and risk stage; (ii) intelligent alerting of the dedicated physician through the construction of medical workflows on-the-fly; and (iii) dynamic adaptation of the vital signs’ monitoring environment on any available device or smart phone located in close proximity to the physician depending on new medical measurements, additional disease specifications or the failure of the infrastructure. The intelligence lies in the adoption of semantics providing for a personalized and automated emergency alerting that smoothly interacts with the physician, regardless of his location, ensuring timely intervention during an emergency. It is evaluated on a medical emergency scenario, where in the case of exceeded patient thresholds, medical personnel are localized and contacted, presenting ad hoc information on the patient’s condition on the most suited device within the physician’s reach.

Presently, with the increasing number and complexity of available gene expression datasets, the combination of data from multiple microarray studies addressing a similar biological question is gaining importance. The analysis and integration of multiple datasets are expected to yield more reliable and robust results since they are based on a larger number of samples and the effects of the individual study-specific biases are diminished. This is supported by recent studies suggesting that important biological signals are often preserved or enhanced by multiple experiments. An approach to combining data from different experiments is the aggregation of their clusterings into a consensus or representative clustering solution which increases the confidence in the common features of all the datasets and reveals the important differences among them.

Existing robot guides offer a tour of a building, such as a museum or science centre, to one or more visitors. Usually the tours are predefined and lack support for dynamic interactions between the different robots. This paper focuses on the distributed collaboration of multiple heterogeneous robots (receptionist, companion) guiding visitors through a building. Semantic techniques support the formal definition of tour topics, the available content on a specific topic, and the robot and person profiles including interests and acquired knowledge. The robot guides select topics depending on their participants' interests and prior knowledge. Whenever one guide moves into the proximity of another, the guides automatically exchange participants, optimizing the amount of interesting topics. Robot collaboration is realized through the development of a software module that allows a robot to transparently include behaviours performed by other robots into its own set of behaviours. The multi-robot visitor guide application is integrated into an extended distributed heterogeneous robot team, using a receptionist robot that was not originally designed to cooperate with the guides. Evaluation of the implemented algorithms presents a 90% content coverage of relevant topics for the participants.

This paper presents a dynamic composer for Web services. The services are enriched with semantic descriptions in OWL-S, based on which the composer automatically creates a combination of services reaching a specified goal. As an example, a trip planning use case is chosen where the goal ranges from booking of a single flight to planning of an entire trip including flight, hotel, transport, etc. The composition is achieved using local and global algorithms satisfying specific quality of service (QoS) constraints and requirements such as the execution time or cost of the invoked Web services. At the same time a more extended HTN planning algorithm is discussed, matching not only service outputs to inputs but also satisfying service preconditions through effects. In addition to the automatic composition, the paper also proposes a recovery mechanism in case of unavailable services. When executing the composition of flight services, unavailable services are dynamically replaced by equivalent services or a new composition achieving the needed result. The presented platform and planning algorithms are put through extensive performance and scalability tests for typical trip booking scenarios, in which basic services are composed to a complex trip planning service.