Game-Framing Cognitive Assessments to Improve Applicant Perceptions

Research has shown that although cognitive testing is key to quality hiring, applicants often react poorly to cognitive ability tests. Applicant reactions theory indicates that time-length judgments of a selection procedure can affect applicant perceptions. It was thus hypothesized that game-framing, the act of labeling something a game without changing the content, would cause participants to perceive that time was moving faster while completing a battery of cognitive ability tests. Similarly, it was expected that game-framing would increase test motivation and decrease test anxiety. Perceived length was tested as a mediator for the effects of game-framing on test anxiety and on test motivation. Structural equation modeling was used to evaluate the hypothesized relationships. In the observed dataset, game-framing caused decreases in perceived length, perceived length was positively related to test motivation, and perceived length mediated the relationship between game-framing and test motivation. The results of this study demonstrate that game-framing affects time perceptions. This finding has implications for gamification researchers, namely, that game-framing effects should be measured and accounted for in future studies. Furthermore, applicant reactions theorists have suggested that perceived time length is a key variable in the overall applicant reactions model, and this study is the first to empirically investigate perceived time length of a selection procedure in this context. Results indicate that perceived length may not relate to other applicant reaction variables as predicted by applicant reactions theory

Exploring the landscape of the space of heuristics for local search in SAT

Local search is a powerful technique on many combinatorial optimisation problems. However, the effectiveness of local search methods will often depend strongly on the details of the heuristics used within them. There are many potential heuristics, and so finding good ones is in itself a challenging search problem. A natural method to search for effective heuristics is to represent the heuristic as a small program and then apply evolutionary methods, such as genetic programming. However, the search within the space of heuristics is not well understood, and in particular little is known of the associated search landscapes. In this paper, we consider the domain of propositional satisfiability (SAT), and a generic class of local search methods called ‘WalkSAT’. We give a language for generating the heuristics; using this we generated over three million heuristics, in a systematic manner, and evaluated their associated fitness values. We then use this data set as the basis for an initial analysis of the landscape of the space of heuristics. We give evidence that the heuristic landscape exhibits clustering. We also consider local search on the space of heuristics and show that it can perform quite well, and could complement genetic programming methods on that space

Systematic search for local-search SAT heuristics

Heuristics for local-search are a commonly used method of improving the performance of algorithms that solve hard computational problems. Generally these are written by human experts, however a long-standing research goal has been to automate the construction of these heuristics. In this paper, we investigate the applicability of a systematic search on the space of heuristics to be used in a local-search SAT solver

Migration and identity in post-referendum Scotland

This paper examines migration and identity in contemporary Scotland and engages with ongoing debates about the relationship between nationalism and cosmopolitanism. The paper employs Arendt’s maxim of the ‘right to have rights’ to suggest that while identity would not be the sole or specific focus of policy, more well-developed social policy attuned to the complexities of identity formation would facilitate multicultural and multi-ethnic social identification

Multiazimuth velocity analysis using velocity-independent seismic imaging

textMultiazimuth seismic data contains information about how the Earth’s seismic response changes with azimuthal direction. Directional-dependence of the seismic response can be caused by anisotropy or heterogeneity, associated with subsurface features such as fractures, stresses, or structure. Characterizing azimuthal variations is done through velocity analysis, which provides a link between an acquired data set and its image, as well as between the image and subsurface geology. At the stage which conventional velocity analysis is applied, it is difficult to distinguish the geologic cause of observed azimuthal velocity variations. The inability to distinguish the similar effects of anisotropy and heterogeneity leads to positioning errors in the final image and velocity estimates. Regardless of the cause, azimuthally variable velocities require at least three parameters to characterize, as opposed to the conventional single-parameter isotropic velocity. The semblance scan is the conventional tool for seismic velocity analysis, but it was designed for the isotropic case. For multiple parameters, the semblance scan becomes computationally impractical. In order to help address the xiissues of geologic ambiguity and computational efficiency, I develop three methods for multiazimuth seismic velocity analysis based on “velocity-independent” imaging techniques. I call this approach, velocity analysis by velocity-independent imaging, where I reverse the conventional order of velocity estimation followed by image estimation. All three methods measure time-domain effective-velocity parameters. The first method, 3D azimuthally anisotropic velocity-independent NMO, replaces the explicit measurement of velocity with local slope detection. The second method, time-warping, uses local slope information to predict traveltime surfaces without any moveout assumption beforehand, and then fit them with a multiparameter velocity model. The third method, azimuthal velocity continuation, uses diffraction image focusing as a velocity analysis criterion, thereby performing imaging and velocity analysis simultaneously. The first two methods are superior to the semblance scan in terms of computational efficiency and their ability to handle multi-parameter models. The third method is similar to a single multi-parameter semblance scan in computational cost, but it helps handle the ambiguity between structural heterogeneity and anisotropy, which leads to better positioned images and velocity estimates.Geological Science

Automated Heuristic Generation By Intelligent Search

This thesis presents research that examines the effectiveness of several different program synthesis techniques when used to automate the creation of heuristics for a local search-based Boolean Satisfiability solver. Previous research focused on the automated creation of heuristics has almost exclusively relied on evolutionary computation techniques such as genetic programming to achieve its goal. In wider program synthesis research, there are many other techniques which can automate the creation of programs. However, little effort has been expended on utilising these alternate techniques in automated heuristic creation. In this thesis we analyse how three different program synthesis techniques perform when used to automatically create heuristics for our problem domain. These are genetic programming, exhaustive enumeration and a new technique called local search program synthesis. We show how genetic programming can create effective heuristics for our domain. By generating millions of heuristics, we demonstrate how exhaustive enumeration can create small, easily understandable and effective heuristics. Through an analysis of the memoized results from the exhaustive enumeration experiments, we then describe local search program synthesis, a program synthesis technique based on the minimum tree edit distance metric. Using the memoized results, we simulate local search program synthesis on our domain, and present evidence that suggests it is a viable technique for automatically creating heuristics. We then define the necessary algorithms required to use local search program synthesis without any reliance on memoized data. Through experimentation, we show how local search program synthesis can be used to create effective heuristics for our domain. We then identify examples of heuristics created that are of higher quality than those produced from other program synthesis methods. At certain points in this thesis, we perform a more detailed analysis on some of the heuristics created. Through this analysis, we show that, on certain problem instances, several of the heuristics have better performance than some state-of-the-art, hand-crafted heuristics