The prediction of thermo-mechanical behaviour of heterogeneous materials such as heat and moisture transport is strongly influenced by the uncertainty in parameters. Such materials occur e.g. in historic buildings, and the durability assessment of these therefore needs a reliable and probabilistic simulation of transport processes, which is related to the suitable identification of material par...

Semantic filtering of multimedia content is a challenging problem. The gap that exists between low-level media features and high-level semantics of multimedia is difficult to bridge. We propose a flexible probabilistic graphical framework to bridge this gap to some extent and perform automatic detection of semantic concepts. Using probabilistic multimedia objects (multijects) and a network of s...

In this paper we discuss a data driven approach to select better phone model topologies, in particular to decide on the number of states for linear left-right continuous HMMs. The novel approach is based on a conditional probabilistic viterbi path estimation and operates on forward-backward trained multiple parallel-path HMMs consisting of two different topologies. We compare this conditional p...

The pervasive use of Location-based-Social-Networks calls for more precise Point-of-Interest recommendation. The probability of a user’s visit to a target place is influenced by multiple factors. Though there are several fusion models in such fields, heterogeneous information are not considered comprehensively. To this end, we propose a novel probabilistic latent factor model by jointly conside...

Multi-sensor data fusion is extensively used to merge data collected by heterogeneous sensors deployed in smart environments. However, data coming from sensors are often noisy and inaccurate, and thus probabilistic techniques, such as Dynamic Bayesian Networks, are often adopted to explicitly model the noise and uncertainty of data. This work proposes to improve the accuracy of probabilistic in...

Automatic human activity detection is one of the difficult tasks in image segmentation application due to variations in size, type, shape and location of objects. In the traditional probabilistic graphical segmentation models, intra and inter region segments may affect the overall segmentation accuracy. Also, both directed and undirected graphical models such as Markov model, conditional random...

Many recommender systems might be part of an e-commerce or multi functional system (or portal) where various information about users, products/documents, social networks, and different types of user feedback about products/documents are available. This paper exploits the heterogeneous information a recommender system might collect to make the most appropriate recommendations. We propose a new P...

System-level fault diagnosis of massively parallel computers requires efficient algorithms, handling a many processing elements in a heterogeneous environment. Probabilistic fault diagnosis is an approach to make the diagnostic problem both easier to solve and more generally applicable. The price to pay for these advantages is that the diagnostic result is no longer guaranteed to be correct and...

A new data fusion method called joint probability density matrix (JPDM) is proposed, which can associate and fuse measurements from spatially distributed heterogeneous sensors to identify the real target in a surveillance region. Using the probabilistic grids representation, we numerically combine the uncertainty regions of all the measurements in a general framework. The NP-hard multisensor da...

A major challenge of pervasive context-aware computing and intelligent environments resides in the acquisition and modelling of rich and heterogeneous context data. Decisive aspects of this information are the ongoing human activities at different degrees of granularity. We conjecture that ontology-based activity models are key to support interoperable multilevel activity representation and rec...