Abstract: In this work we present a new process distribution approach for sensor data fusion (SDF) systems, called FuseDis (fusion distribution using tessellated space). It is based on a hybrid partitioning approach, aimed at managing computational burden and achieving scalability.
First, a functional decomposition dicides SDF functionality into taskgroups, vectorizing operations. Second, a partitioning of the dataspace, based on geographic attributes of the data, is applied to parallelize the processing.
A tesselation applied to the plot-space imlicitly defines for each tile the set of candidate-tracks yielding useful correlations with the plots in a tile. Some tracks may occur as correlation-candidates for multiple tiles. Conflicts caused by correlations of such tracks with plots in different tiles are solved by combining correlations of the involved tracks and plots into independent association problems
Abstract: Each person holds numerous values that represent what is
believed to be important. As a result, our values inﬂuence our behavior
and play a role in practical reasoning. Various argumentation approaches
use values to justify actions, but they assume a function that determines
what values a state or action promotes and demotes. However, this is
often open for debate, since values are abstract and can be interpreted
in many ways. After giving an overview of how values are deﬁned in
social psychology, this paper deﬁnes values as preferences and introduces
several argument schemes to reason about preferences. These schemes
are used to give meaning to values and to determine whether values are
promoted or demoted. Furthermore, value systems are used for practical
reasoning and allow resolving conﬂicts when pursuing your values. An
example is given of how the new argument schemes can be used to do
practical reasoning using values.