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Currently available papers

Mouse Sensitivity in First-Person Targeting Tasks

Mouse sensitivity in first-person targeting tasks is a highly debated issue. Recommendations within a single game can vary by a factor of 10× or more and are an active topic of experimentation in both competitive and recreational esports communities. …

Generating Interpretable Play-Style Descriptions Through Deep Unsupervised Clustering of Trajectories

In any game, play style is a concept that describes the technique and strategy employed by a player to achieve a goal. Identifying a player's style is desirable as it can enlighten players on which approaches work better or worse in different …

Compressing and Comparing the Generative Spaces of Procedural Content Generators

The past decade has seen a rapid increase in the level of research interest in procedural content generation (PCG) for digital games, and there are now numerous research avenues focused on new approaches for driving and applying PCG systems. An area …

Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry

Deep generative models can automatically create content of diverse types. However, there are no guarantees that such content will satisfy the criteria necessary to present it to end-users and be functional, e.g. the generated levels could be …

Mining Node.js Vulnerabilities via Object Dependence Graph and Query

Node.js is a popular non-browser JavaScript platform that provides useful but sometimes also vulnerable packages. On one hand, prior works have proposed many program analysis-based approaches to detect Node.js vulnerabilities, such as command …

GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters

Playing games with cheaters is not fun, and in a multibillion-dollar video game industry with hundreds of millions of players, game developers aim to improve the security and, consequently, the user experience of their games by preventing cheating. …

Finding Bugs Using Your Own Code: Detecting Functionally-similar yet Inconsistent Code

Probabilistic classification has shown success in detecting known types of software bugs. However, the works following this approach tend to require a large amount of specimens to train their models. We present a new machine learning-based bug …

A generalizability measure for program synthesis with genetic programming

The generalizability of programs synthesized by genetic programming (GP) to unseen test cases is one of the main challenges of GP-based program synthesis. Recent work showed that increasing the amount of training data improves the generalizability of …

Genetic Adversarial Training of Decision Trees

We put forward a novel learning methodology for ensembles of decision trees based on a genetic algorithm that is able to train a decision tree for maximizing both its accuracy and its robustness to adversarial perturbations. This learning algorithm …

A Deep Dive into Deep Learning Approaches for Text-to-SQL Systems

Data is a prevalent part of every business and scientific domain, but its explosive volume and increasing complexity make data querying challenging even for experts. For this reason, numerous text-to-SQL systems have been developed that enable …