Oral cancer is the predominant neoplasm of the head and neck. Annually, more than 500.000 new cases of oral cancer are reported, worldwide. After the initial treatment of cancer and its complete disappearance, a state called remission, reoccurrence rates still remain quite high and the early identification of such relapses is a matter of great importance. Up to now, several approaches have been...

Diagnosis and prediction m some domains, like medical and industrial diagnosis, require a representation that combines uncertainty management and temporal reasoning. Based on the fact that in many cases there are few state changes in the temporal range of interest, we propose a novel represen­ tation called Temporal Nodes Bayesian Network (TNBN). In a TNBN each node represents an event or state...

In this paper we introduce a new dynamic Bayesian network that separates the speakers and their speaking turns in a multi-person conversation. We protect the speakers’ privacy by using only features from which intelligible speech cannot be reconstructed. The model we present combines data from multiple audio streams, segments the streams into speech and silence, separates the different speakers...

We present further developments in our work on using data from real users to build a probabilistic model of user affect based on Dynamic Bayesian Networks (DBNs) and designed to detect multiple emotions. We present analysis and solutions for inaccuracies identified by a previous evaluation; refining the model’s appraisals of events to reflect more closely those of real users. Our findings lead ...

Systems of ordinary differential equations (ODEs) are often used to model the dynamics of complex biological pathways. We construct a discrete state model as a probabilistic approximation of the ODE dynamics by discretizing the value space and the time domain. We then sample a representative set of trajectories and exploit the discretization and the structure of the signaling pathway to encode ...

When given a single frame of the video, humans can not only interpret the content of the scene, but also they are able to forecast the near future. This ability is mostly driven by their rich prior knowledge about the visual world, both in terms of (i) the dynamics of moving agents, as well as (ii) the semantic of the scene. In this work we exploit the interplay between these two key elements t...

The usual methods of applying Bayesian networks to the modeling of temporal processes, such as Dean and Kanazawa’s dynamic Bayesian networks (DBNs), consist in discretizing time and creating an instance of each random variable for each point in time. We present a new approach called network of probabilistic events in discrete time (NPEDT), for temporal reasoning with uncertainty in domains invo...

background: high-risk unsafe behaviors (hrubs) have been known as the main cause of occupational accidents. considering the financial and societal costs of accidents and the limitations of available resources, there is an urgent need for managing unsafe behaviors at workplaces. the aim of the present study was to find strategies for decreasing the rate of hrubs using an integrated approach of s...

Given an input structure, a relational Bayesian network returns a Bayesian network over ground atoms. In this paper, we analyze the problem of checking the consistency of a relational Bayesian network for a given class of input structures. This consistency is defined as the acyclicity of the output Bayesian network. We employ first-order logic to encode both the input structure and the structur...