Heart sounds are difficult to interpret due to events with very short temporal onset between them (tens of millisec- onds) and dominant frequencies that are out of the human audi- ble spectrum. Computer assisted decision systems may help but they require robust signal processing algorithms. In this paper, we propose a new algorithm for heart sound segmentation using a hidden semi-Markov model. The proposed algorithm infers more suitable sojourn time parameters than those currently suggested by the state-of-art, through a maximum likelihood approach. We test our approach over three different datasets, including the publicly available PhysioNet and Pascal datasets. We also release a pediatric dataset composed of 29 heart sounds. In contrast with any other dataset available online, the annotations of the heart sounds in the released dataset contain information about the beginning and the ending of each heart sound event. Annotations were made by two cardiopulmonologists. The proposed algorithm is compared with the current state- of-the-art. The results show a significant increase in segmen- tation performance, regardless the dataset or the methodology presented. E.g.: when using the PhysioNet dataset to train and to evaluate the HSMMs, our algorithm achieved average an F-score of 92% compared to 89% achieved by the algorithm described in [1]. In this sense, the proposed approach to adapt sojourn time parameters represents an effective solution for heart sound segmentation problems, even when the training data does not perfectly express the variability of the testing data.

Abstract. Probabilistic Inductive Logic Programming (PILP) is a relatively unexplored area of Statistical Relational Learning which extends classic Inductive Logic Programming (ILP). This work introduces SkILL, a Stochastic Inductive Logic Learner, which takes probabilistic annotated data and produces First Order Logic theories. Data in several domains such as medicine and bioinformatics have an inherent degree of uncertainty, that can be used to produce models closer to reality. SkILL can not only use this type of probabilistic data to extract non-trivial knowledge from databases, but it also addresses efficiency issues by introducing a novel, efficient and effective search strategy to guide the search in PILP environments. The capabilities of SkILL are demonstrated in three different datasets: (i) a synthetic toy example used to validate the system, (ii) a probabilistic adaptation of a well-known biological metabolism application, and (iii) a real world medical dataset in the breast cancer domain. Results show that SkILL can perform as well as a deterministic ILP learner, while also being able to incorporate probabilistic knowledge that would otherwise not be considered.

Vehicle-to-vehicle and vehicle-to-infrastructure communication systems enable vehicles to share information captured by their local sensors with other interested vehicles. To ensure that this information is delivered at the right time and location, context-aware routing is vital for intelligent inter-vehicular communication. Traditional network addressing and routing schemes do not scale well for large vehicular networks. The conventional network multicasting and broadcasting cause significant overhead due to a large amount of irrelevant and redundant transmissions. To address these challenges, we first take into account contextual properties such as location, direction, and information interest to reduce the network traffic overhead. Second, to improve the relevancy of the received information we leverage the mobility patterns of vehicles and the road layouts to further optimize the peer-to-peer routing of the information. Third, to ensure our approach is scalable, we propose a context-based grouping mechanism in which relevant information is shared in an intelligent way within and between the groups. We evaluate our approach based on groups with common spatio-temporal characteristics. Our simulation experiments show that our context-based routing scheme and grouping mechanism significantly reduces the propagation of irrelevant and redundant information.

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This paper presents an interdisciplinary study joining insights of landscape architecture and computer vision. In this work we used a dataset of contemplative landscape images that was collected and evaluated by experts in landscape architecture. We used the dataset to develop nine kmeans clustering and one K-nearest neighbors models that are able to score landscape images based on seven different landscape image features (layers, landform, vegetation, color and light, compatibility, archetypal elements, character of peace and silence) that were identified as contributing to the overall contemplativeness of a landscape. Finally, we chose the combination of models that would produce the best combined contemplativeness score and created CLASS a scoring system that can evaluate the contemplativeness of landscape images with scores similar to those of experts. The authors would like to thank the anonymous reviewers for their valuable input to the quality of the paper. They are also grateful to the European Program for Young Entrepreneurs (EYE Program) for funding that enabled our international team to work together.

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We present a novel approach that uses an iterative deepening algorithm in order to perform probabilistic logic inference for ProbLog, a probabilistic extension of Prolog. The most used inference method for ProbLog is exact inference combined with tabling. Tabled exact inference first collects a set of SLG derivations which contain the probabilistic structure of the ProbLog program including the cycles. At a second step, inference requires handling these cycles in order to create a noncyclic Boolean representation of the probabilistic information. Finally, the Boolean representation is compiled to a data structure where inference can be performed in linear time. Previous work has illustrated that there are two limiting factors for ProbLog's exact inference. The first factor is the target compilation language and the second factor is the handling of the cycles. In this paper, we address the second factor by presenting an iterative deepening algorithm which handles cycles and produces solutions to problems that previously ProbLog was not able to solve. Our experimental results show that our iterative deepening approach gets approximate bounded values in almost all cases and in most cases we are able to get the exact result for the same or one lower scaling factor.

This section analyzes the worst case complexity of our preprocessing algorithms. We use N to denote the number of proofs or conjunctions and M to denote the number of probabilistic facts or Boolean variables. By our experience, N >> M usually holds for typical ProbLog programs.