In agriculture Markov decision processes (MDPs) with finite state and action space are often used to model sequential decision making over time. For instance, states in the process represent possible levels of traits of the animal and transition probabilities are based on biological models estimated from data collected from the animal or herd. State space models (SSMs) are a general tool for mo...

●Objective: Transform spoken-style language (V) into written style language (W) for the creation of transcripts ●Approach: Statistical machine translation to “translate” from verbatim text to written text ●Innovations: ●Log-linear modeling for improved accuracy ●Introduction of features to handle common phenomena in speaking-style transformation ●WFST-based implementation for integration with W...

bearing capacity prediction of axially loaded piles is one of the most important problems in geotechnical engineering practices, with a wide variety range of methods which have been introduced to forecast it accurately. machine learning methods have been reported by many contemporary researches with some degree of success in modeling geotechnical phenomena. in this study, a fairly new machine l...

Scenario models and hierarchical state machines play key roles in current object-oriented modeling methodologies. Our work maily focuses on a systematic transition between these two models. In this paper, we argue for the need to develop a catalog of design patterns [18,19] for state machine implementation. The originality of our proposal stems from having these patterns rooted in specific beha...

In this brief position paper we argue that model-driven engineering practices could be adopted in the design and evaluation of automotive UI. We illustrate how UML state machine models can be used for automatic generation of executable prototypes of the UI and for computing graph-theoretic metrics that could bear upon cognitive load.

A compact symbolic encoding is described for the transition relation of systems modeled with asynchronously executing, hierarchical UML state machines that communicate through message passing and attribute access. This enables the analysis of such systems by symbolic model checking techniques, such as BDD-based model checking and SATbased bounded model checking. Message reception, completion ev...

We introduce a new formal computational model designed for studying the information transfer among the generations of offspring-producing evolving machines — so-called autopoietic automata. These can be seen as nondeterministic finite state transducers whose “program” can become a subject of their own processing. An autopoietic automaton can algorithmically generate an offspring controlled by a...

Existing algorithms for regular inference (aka automata learning) allows to infer a finite state machine model of a system under test (SUT) by observing the output that the SUT produces in response to selected sequences of input. In this paper we present an approach using regular inference to construct models of communication protocol entities. Entities of communication protocols typically take...

In this thesis several possibilities are investigated for improving the performance of Liquid State Machines. A Liquid State Machine is a relatively new system that is a Machine Learning system, which is capable of coping with temporal dependencies. Basic Recurrent Neural Networks often have problems with this. One reason for this is that it takes a long time to train the Recurrent Neural Netwo...