Staff Pay Levels for Selected Positions in House Member Offices, 2009-2013

[Excerpt] This report provides pay data for 13 staff position titles that are typically deployed in the offices of Members of the House. The positions include the following: Caseworker; Chief of Staff; Counsel; District Director; Executive Assistant; Field Representative; Legislative Assistant; Legislative Correspondent; Legislative Director; Office Manager; Press Secretary; Scheduler; and Staff Assistant. House Member staff pay data for the years 2009-2013 were developed based on a random sampling of staff for each position in each year

Staff Pay Levels for Selected Positions in Senators’ Offices, FY2009-FY2013

[Excerpt] This report provides pay data for 16 staff position titles that are typically deployed in Senators’ offices. The positions include the following: Administrative Director; Casework Supervisor; Caseworker; Chief of Staff; Communications Director; Counsel; Executive Assistant; Field Representative; Legislative Assistant; Legislative Correspondent; Legislative Director; Press Secretary; Scheduler; “Specials Director,” a combined category that includes the job titles Director of Projects, Director of Special Projects, Director of Federal Projects, Director of Grants, Projects Director, or Grants Director; Staff Assistant; and State Director. Senators’ staff pay data for the years FY2009-FY2013 were derived from a random sampling of Senators’ offices in which at least one staff member worked in a position in each year

Knowledge web: realising the semantic web... all the way to knowledge-enhanced multimedia documents

The semantic web and semantic web services are major efforts in order to spread and to integrate knowledge technology to the whole web. The Knowledge Web network of excellence aims at supporting their developments at the best and largest European level and supporting industry in adopting them. It especially investigates the solution of scalability, heterogeneity and dynamics obstacles to the full development of the semantic web. We explain how Knowledge Web results should benefit knowledge-enhanced multimedia applications

A spatially-structured PCG method for content diversity in a Physics-based simulation game

This paper presents a spatially-structured evolutionary algorithm (EA) to procedurally generate game maps of di ferent levels of di ficulty to be solved, in Gravityvolve!, a physics-based simulation videogame that we have implemented and which is inspired by the n- body problem, a classical problem in the fi eld of physics and mathematics. The proposal consists of a steady-state EA whose population is partitioned into three groups according to the di ficulty of the generated content (hard, medium or easy) which can be easily adapted to handle the automatic creation of content of diverse nature in other games. In addition, we present three fitness functions, based on multiple criteria (i.e:, intersections, gravitational acceleration and simulations), that were used experimentally to conduct the search process for creating a database of maps with di ferent di ficulty in Gravityvolve!.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Using Self-Adaptive Evolutionary Algorithms to Evolve Dynamism-Oriented Maps for a Real Time Strategy Game

9th International Conference on Large Scale Scientific Computations. The final publication is available at link.springer.comThis work presents a procedural content generation system that uses an evolutionary algorithm in order to generate interesting maps for a real-time strategy game, called Planet Wars. Interestingness is here captured by the dynamism of games (i.e., the extent to which they are action-packed). We consider two different approaches to measure the dynamism of the games resulting from these generated maps, one based on fluctuations in the resources controlled by either player and another one based on their confrontations. Both approaches rely on conducting several games on the map under scrutiny using top artificial intelligence (AI) bots for the game. Statistic gathered during these games are then transferred to a fuzzy system that determines the map's level of dynamism. We use an evolutionary algorithm featuring self-adaptation of mutation parameters and variable-length chromosomes (which means maps of different sizes) to produce increasingly dynamic maps.TIN2011-28627-C04-01, P10-TIC-608