This paper presents a methodology for real-time estimation of water distribution system state parameters using a dynamic Bayesian network to combine current observations with knowledge of past system behavior. The dynamic Bayesian network presented here allows the flexibility to model both discrete and continuous variables and represent causal relationships that exist within the distribution sy...

Image location prediction is to estimate the geolocation where an image is taken. Social image contains heterogeneous contents, which makes image location prediction nontrivial. Moreover, it is observed that image content patterns and location preferences correlate hierarchically. Traditional image location prediction methods mainly adopt a single-level architecture, which is not directly adapt...

Welcome to Odyssey 2012: The Speaker and Language Recognition Workshop, hosted by COLIPS (Chinese and Oriental Languages Information Processing Society) in Singapore, on 25-28 June 2012. Odyssey 2012 received overwhelming response from the speaker and language recognition community. We accepted 51 papers out of 65 submissions, which we organized into a 4-day technical program consisting of 11 s...

Expert search is a task of growing importance in Enterprise settings. In a classical search setting, users normally require relevant documents to fulfil an information need. However, in Enterprise settings, users also have a need to identify the co-workers with relevant expertise to a topic area. An expert search engine assists users with their expertise need, by ranking candidate experts with ...

With the increase in environmental awareness, competitions and government policies, implementation of green supply chain management activities to sustain production and conserve resources is becoming more necessary for different organizations. However, it is difficult to successfully implement green supply chain (GSC) activities because of the risks involved. These risks alongside their resourc...

Training deep belief networks (DBNs) is normally done with large data sets. Our goal is to predict traces of the surface of the tongue in ultrasound images of human speech. Hand-tracing is labor-intensive; the dataset is highly imbalanced since many images are extremely similar. We propose a bootstrapping method which handles this imbalance by iteratively selecting a small subset of images to b...

Bayesian belief networks or causal probabilistic networks may reach a certain size and complexity where the computations involved in exact probabilistic inference on the network tend to become rather time consuming. Methods for approximating a network by a simpler one allow the computational complexity of probabilistic inference on the network to be reduced at least to some extend. We propose a...

Graphical models have become common for representing probabilistic models in statistics and artificial intelligence. A Bayesian network is a graphical model which encodes a probability model as a directed graph in which nodes correspond to random variables, together with a set of conditional distributions of nodes given their parents. In most current applications of Bayesian networks, a fixed n...

We investigate properties of Bayesian networks (BNs) in the context of state estimation. We introduce a coarse perspective on the inference processes and use this perspective to identify conditions under which state estimation with BNs can be very robust, even if the quality of the model is very low. By making plausible assumptions we can formulate asymptotic properties of the estimation perfor...