Efficient computation of updated lower expectations for imprecise continuous-time hidden Markov chains

We consider the problem of performing inference with imprecise continuous-time hidden Markov chains, that is, imprecise continuous-time Markov chains that are augmented with random output variables whose distribution depends on the hidden state of the chain. The prefix `imprecise' refers to the fact that we do not consider a classical continuous-time Markov chain, but replace it with a robust extension that allows us to represent various types of model uncertainty, using the theory of imprecise probabilities. The inference problem amounts to computing lower expectations of functions on the state-space of the chain, given observations of the output variables. We develop and investigate this problem with very few assumptions on the output variables; in particular, they can be chosen to be either discrete or continuous random variables. Our main result is a polynomial runtime algorithm to compute the lower expectation of functions on the state-space at any given time-point, given a collection of observations of the output variables

Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. Our research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth

Model prediction of subendocardial perfusion of the coronary circulation in the presence of an epicardial coronary artery stenosis

The subendocardium is most vulnerable to ischemia, which is ameliorated by relaxation during diastole and increased coronary pressure. Recent clinical techniques permit the measuring of subendocardial perfusion and it is therefore important to gain insight into how measurements depend on perfusion conditions of the heart. Using data from microsphere experiments a layered model of the myocardial wall was developed. Myocardial perfusion distribution during hyperemia was predicted for different degrees of coronary stenosis and at different levels of Diastolic Time Fraction (DTF). At the reference DTF, perfusion was rather evenly distributed over the layers and the effect of the stenosis was homogenous. However, at shorter or longer DTF, the subendocardium was the first or last to suffer from shortage of perfusion. It is therefore concluded that the possible occurrence of subendocardial ischemia at exercise is underestimated when heart rate is increased and DTF is lower

Supporting Smart Home Scenarios Using OWL and SWRL Rules

Despite the pervasiveness of IoT domotic devices in the home automation landscape, their potential is still quite under-exploited due to the high heterogeneity and the scarce expressivity of the most commonly adopted scenario programming paradigms. The aim of this study is to show that Semantic Web technologies constitute a viable solution to tackle not only the interoperability issues, but also the overall programming complexity of modern IoT home automation scenarios. For this purpose, we developed a knowledge-based home automation system in which scenarios are the result of logical inferences over the IoT sensors data combined with formalised knowledge. In particular, we describe how the SWRL language can be employed to overcome the limitations of the well-known trigger-action paradigm. Through various experiments in three distinct scenarios, we demonstrated the feasibility of the proposed approach and its applicability in a standardised and validated context such as SARE

Expertise-based peer selection in Peer-to-Peer networks

Peer-to-Peer systems have proven to be an effective way of sharing data. Modern protocols are able to efficiently route a message to a given peer. However, determining the destination peer in the first place is not always trivial. We propose a a message to a given peer. However, determining the destination peer in the first place is not always trivial. We propose a model in which peers advertise their expertise in the Peer-to-Peer network. The knowledge about the expertise of other peers forms a semantic topology. Based on the semantic similarity between the subject of a query and the expertise of other peers, a peer can select appropriate peers to forward queries to, instead of broadcasting the query or sending it to a random set of peers. To calculate our semantic similarity measure, we make the simplifying assumption that the peers share the same ontology. We evaluate the model in a bibliographic scenario, where peers share bibliographic descriptions of publications among each other. In simulation experiments complemented with a real-world field experiment, we show how expertise-based peer selection improves the performance of a Peer-to-Peer system with respect to precision, recall and the number of messages

The OpenKnowledge System: An Interaction-Centered Approach to Knowledge Sharing

Abstract. The information that is made available through the semantic web will be accessed through complex programs (web-services, sensors, etc.)thatmayinteract in sophisticated ways. Composition guided simply by the specifications of programs ’ inputs and outputs is insufficient to obtain reliable aggregate performance- hence the recognised need for process models to specify the interactions required between programs. These interaction models, however, are traditionally viewed as a consequence of service composition rather than as the focal point for facilitating composition. We describe an operational system that uses models of interaction as the focus for knowledge exchange. Our implementation adopts a peer to peer architecture, thus making minimal assumptions about centralisation of knowledge sources, discovery and interaction control.

Особливості формування етнічного складу селянської верстви Степового Побужжя

In this short paper we sketch a brief introduction to our Krimp algorithm. Moreover, we briefly discuss some of the large body of follow up research. Pointers to the relevant papers are provided in the bibliography