Complex tasks for heterogeneous data sources, such as finding and linking named entities in text documents or detecting objects in images, often require multiple steps to be solved in a processing pipeline. In most of the cases, there exist numerous, exchangeable software components for a single step, each an ”expert” for data with certain characteristics. Which expert to apply to which observe...

Nonlinear Model Predictive Control (NMPC) has gained wide attention through the application of dynamic optimization. However, this approach is susceptible to computational delay, especially if the optimization problem cannot be solved within one sampling time. In this paper we propose an advanced-multi-step NMPC (amsNMPC) method based on nonlinear programming (NLP) and NLP sensitivity. This met...

Recent years have seen the development of efficient and provably correct spectral algorithms for learning models of partially observable environments arising in many applications. But despite the high hopes raised by this new class of algorithms, their practical impact is still below expectations. One reason for this is the difficulty in adapting spectral methods to exploit structural constrain...

Unifying seemingly disparate algorithmic ideas to produce better performing algorithms has been a longstanding goal in reinforcement learning. As a primary example, TD(λ) elegantly unifies one-step TD prediction with Monte Carlo methods through the use of eligibility traces and the trace-decay parameter λ. Currently, there are a multitude of algorithms that can be used to perform TD control, in...

Current techniques employed in security alert correlation area for multi-step attack recognition purpose are intricate to be performed due to the complexity of the methods and huge computing workload generated during alert analysis and processing. In this paper, we proposed a new method of alert correlation aiming at providing concentrated security event information and thus finding multi-step ...

A predictive forward-dynamic simulation of human gait would be extremely useful to many different researchers, and professionals. Metabolic efficiency is one of the defining characteristics of human gait. Forward-dynamic simulations of human gait can be used to calculate the muscle load profiles for a given walking pattern, which in turn can be used to estimate metabolic energy consumption. One...

This paper investigates the possibility of obtaining long-into-the-future reliable forecasts of observed nonlinear cyclical phenomena. Unsmoothed monthly sunspot numbers that are characteristically cyclical with nonlinear dynamics as well as their wavelet-transformed and wavelet-denoised series are forecasted through October 2008. The objective is to determine whether modelling wavelet-conversi...

In reinforcement learning for multi-step problems, the sparse nature of the feedback aggravates the difficulty of learning to perform. This paper explores the use of a reinforcement learning architecture, leading to a discussion of reinforcement learning in terms of feature abstraction, credit-assignment, and temporal-difference learning. Issues discussed include: the conditioning of the reinfo...

Virtual view synthesis is essential for 3D teleconference systems like VIRTUE, which utilizes the motion parallax cue. In this paper we propose a multi-step view synthesis algorithm to efficiently reconstruct an arbitrary view in real time and utilize all the information we have from stereo cameras. We show that the newly proposed algorithm can be used to reconstruct a high quality view from an...

Perspective camera calibration has been i n the last decades a research subject for a large group of researchers and as a result several camera calibration methodologies can be found i n the literature. However only a small number of those methods base their approaches o n the use of monoplane calibration points. This paper describes one of those methodologies that uses monoplane calibration po...