This paper presents further research on an implemented classification and verification system that employs a novel approach for stochastically modelling movement trajectories. The models are based on Conversive Hidden non-Markovian Models that are especially suited to mimic temporal dynamics of time series as in contrast to the relative Hidden Markov Models(HMM) and the dynamic time warping(DTW) method, timestamp information of data are an integral part. The system is able to create trajectory models from examples and is tested on signatures, doodles and pseudo-signatures for its verification performance. By using publicly available databases comparisons are made to evaluate the potential of the system. The results reveal that the system already performs similar to a general DTW approach on doodles and pseudo-signatures but does not reach the performance of specialized HMM systems for signatures. But further possibilities to improve the results are discussed.

Previous research has shown that diversity within distributed collaborative teams can lead to innovation, but trust must exist for the open expression of innovative ideas and establishment of idea credibility. Initial trust is pivotal for distributed teams where team members have never met face-to-face and have only a very limited time to accomplish a task. Our goal is to determine if knowing specific information about other team members could enhance initial trust and improve productivity and satisfaction in idea generation and idea evaluation sessions. In an experiment, we showed that cognitive and affective trust could be successfully enhanced by presenting relevant information elements, such as domain expertise and personal hobbies, and could have positive effects on the quality and quantity of ideas in idea generation sessions as well as the satisfaction of the participants with the rating result in idea evaluation sessions. However, participants receiving personal information often misconstrue this as professional competency. We also describe gender differences observed in the idea generation sessions and discuss how to better design future systems for supporting idea generation and idea evaluation activities.

A new iterative algorithm, the multi-level algorithm, for the numerical solution of steady state Markov chains is presented. The method utilizes a set of recursively coarsened representations of the original system to achieve accelerated convergence. It is motivated by multigrid methods, which are widely used for fast solution of partial differential equations. Initial results of numerical experiments are reported, showing significant reductions in computation time, often an order of magnitude or more, relative to the Gauss-Seidel and optimal SOR algorithms for a variety of test problems. It is shown how the well-known iterative aggregation-disaggregation algorithm of Takahashi can be interpreted as a special case of the new method. 1. Introduction Markov systems generated by computer modeling tools such as queueing networks, Petri nets, or reliability modeling packages may contain hundreds of thousands of states. The resulting sparse linear systems of equations have a correspondingl...