A hybrid of two novel methods - additive fuzzy spectral clustering and lifting method over a taxonomy - is applied to analyse the research activities of a department. To be specific, we concentrate on the Computer Sciences area represented by the ACM Computing Classification System (ACM-CCS), but the approach is applicable also to other taxonomies. Clusters of the taxonomy subjects are extracted using an original additive spectral clustering method involving a number of model-based stopping conditions. The clusters are parsimoniously lifted then to higher ranks of the taxonomy by minimizing the count of “head subjects” along with their “gaps” and “offshoots”. An example is given illustrating the method applied to real-world data.
CSEE 2014, is to bring together innovative academics and industrial experts in the field of Computer Science and Electronics Engineering to a common forum.
The primary goal of the conference is to promote research and developmental activities in Computer Science and Electronics Engineering. Another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Computer Science and Electronics Engineering and related areas.
The number of space objects will grow several times in a few years due to the planned launches of constellations of thousands microsatellites. It leads to a significant increase in the threat of satellite collisions. Spacecraft must undertake collision avoidance maneuvers to mitigate the risk. According to publicly available information, conjunction events are now manually handled by operators on the Earth. The manual maneuver planning requires qualified personnel and will be impractical for constellations of thousands satellites. In this paper we propose a new modular autonomous collision avoidance system called "Space Navigator". It is based on a novel maneuver optimization approach that combines domain knowledge with Reinforcement Learning methods.
I give the explicit formula for the (set-theoretical) system of Resultants of m+1 homogeneous polynomials in n+1 variables