Localization is a difficult problem to be solved because of the demanding requirements of low cost, high energy efficiency, scalability for the network size and the practical issues associated with node deployments. A probabilistic-based approach is used for localization, where the system determines probability of the target that can be located at any point within the environment. After determi...

With the demand on providing automatic speech recognition (ASR) systems for many markets the question of porting an ASR system to a new language is of practical interest. To cope with this task the adaptation of hidden Markov models (HMM) is seen as a key step to transfer the models from a source to a target language. In this work we introduce a novel adaptation scheme for semi-continuous HMMs ...

This paper explores the use of the homotopy method for training a semi-supervised Hidden Markov Model (HMM) used for sequence labeling. We provide a novel polynomial-time algorithm to trace the local maximum of the likelihood function for HMMs from full weight on the labeled data to full weight on the unlabeled data. We present an experimental analysis of different techniques for choosing the b...

Distributed Denial of Service (DDoS) attacks have caused continuous critical threats to the Internet services. DDoS attacks are generally conducted at the network layer. Many DDoS attack detection methods are focused on the IP and TCP layers. However, they are not suitable for detecting the application layer DDoS attacks. In this paper, we propose a scheme based on web user browsing behaviors t...

This paper addresses the problem of Hidden Markov Models (HMM) training and inference when the training data are composed of feature vectors plus uncertain and imprecise labels. The “soft” labels represent partial knowledge about the possible states at each time step and the “softness” is encoded by belief functions. For the obtained model, called a Partially-Hidden Markov Model (PHMM), the tra...

In this paper we propose a new method for identifying processing stages in human information processing. Since the 1860s scientists have used different methods to identify processing stages, usually based on reaction time (RT) differences between conditions. To overcome the limitations of RT-based methods we used hidden semi-Markov models (HSMMs) to analyze EEG data. This HSMM-EEG methodology c...

The variability of structure in a finite Markov equivalence class of causally sufficient mod­ els represented by directed acyclic graphs has been fully characterized. Without causal suf­ ficiency, an infinite semi-Markov equivalence class of models has only been characterized by the fact that each model in the equiva­ lence class entails the same marginal statis­ tical dependencies. In this pap...

In this paper we extend the predicate logic introduced in [Beauquier et al. 2002] in order to deal with Semi-Markov Processes. We prove that with respect to qualitative probabilistic properties, model checking is decidable for this logic applied to SemiMarkov Processes. Furthermore we apply our logic to Probabilistic Timed Automata considering classical and urgent semantics, and considering als...