Video gameplay, personality and academic performance

The relationship between video gameplay, video game genre preference, personality, and GPA was investigated in an online correlational study with university students. In addition to administering self-report measures of GPA and personality, we asked three different questions regarding styles of video gameplay. The first asked the average time spent playing video games per week (habitual players), the second asked the total time spent playing favorite video games (selective players), and the third asked the number of different video games played in a year (diverse players). Students who were medium in selective player style (spent 11-50 h) had significantly higher GPAs than students low on selective player style (spent 0-10 h). Students high on habitual playing style (7 or more hours a week) showed significantly lower levels of Conscientiousness compared to students low on habitual playing style (0-1 h a week). Students who were high on the diverse style (i.e., 7 or more games played a year) showed significantly higher Openness scores than students low on the diverse style (0-3 games a year). Finally, several notable relations were found between video game genre preference, GPA, and personality. Results are discussed in terms of the positive implications of video gameplay on academic performance.     

Non-linear Control of Heave for an Unmanned Helicopter Using a Neural Network

This paper describes a new non-linear control technique applied to the heave control of an unmanned rotorcraft. First a hybrid plant model consisting of exactly known dynamics is combined with a black-box representation of the unknown dynamics. Desired trajectories are calculated to smoothly achieve a sequence of random step changes in desired height according to certain optimal criterion and plant limitations. Control inputs are then determined using the MATLAB^® optimisation toolbox to achieve those desired trajectories for the plant heave model. Finally, a neural network is trained to mimic the control inputs resulting from the optimisation process. The neural network controller produces trajectories closely resembling the results from the optimisation process but with a much reduced computation time. Flight test results of control of the heave dynamics of a helicopter confirm the neural network controller’s ability to operate in high disturbance conditions and outperform a proportional-derivative (PD) controller.     

A Continuous Local Motion Planning Framework for Unmanned Vehicles in Complex Environments

As the complexity of an unmanned vehicle’s operational environment increases so does the need to consider the obstacle space continually, and this is aided by splitting the motion planning functionality into distinct global and local layers. This paper presents a new continuous local motion planning framework, where the output and control space elements of the traditional receding horizon control problem are separated into distinct layers. This separation reduces the complexity of the local motion trajectory optimisation, enabling faster design and increased horizon length. The focus of this paper is on the output space component of this framework. Bezier polynomial functions are used to describe local motion trajectories which are constrained to vehicle performance limits and optimised to track a global trajectory. Development and testing is in simulation, targeted at a nonlinear model of a quadrotor unmanned air vehicle. The defined framework is used to provide situation-aware tracking of a global trajectory in the presence of static and dynamic obstacles, as well as realistic turbulence and gusts. Also demonstrated is the immediate-term decentralised deconfliction of multiple unmanned vehicles, and multiple formations of unmanned vehicles.     

Mixing Monte Carlo and progressive rendering for improved global illumination

In this paper, we seek to eliminate the noise caused by caustic paths during progressive Monte Carlo path tracing. We employ a filtering strategy over path space, handling each subspace using specialized derivations of path tracing and progressive photon mapping. Evaluating diffuse paths with path tracing allows the use of sample stratification over both pixels and the image as a whole, whilst sharp detailed caustics are produced using progressive photon mapping. This is an efficient, low noise progressive algorithm with vanishing bias combining the advantages of both Monte Carlo methods, and particle tracing.     

Succinct Representation of Labeled Graphs

In many applications, the properties of an object being modeled are stored as labels on vertices or edges of a graph. In this paper, we consider succinct representation of labeled graphs. Our main results are the succinct representations of labeled and multi-labeled graphs (we consider planar triangulations, planar graphs and k-page graphs) to support various label queries efficiently. The additional space cost to store the labels is essentially the information-theoretic minimum. As far as we know, our representations are the first succinct representations of labeled graphs. We also have two preliminary results to achieve the main contribution. First, we design a succinct representation of unlabeled planar triangulations to support the rank/select of edges in ccw (counter clockwise) order in addition to the other operations supported in previous work. Second, we design a succinct representation for a k-page graph when k is large to support various navigational operations more efficiently. In particular, we can test the adjacency of two vertices in O(lg?k) time, while previous work uses O(k) time.     

Exploring probabilistic bisimulations, part I

We take a fresh look at strong probabilistic bisimulations for processes which exhibit both non-deterministic and probabilistic behaviour. We suggest that it is natural to interpret such processes as distributions over states in a probabilistic labelled transition system, a pLTS; this enables us to adapt the standard notion of contextual equivalence to this setting. We then prove that a novel form of bisimulation equivalence between distributions are both sound and complete with respect to this contextual equivalence. We also show that a very simple extension to HML, Hennessy–Milner Logic, provides finite explanations for inequivalences between distributions. Finally we show that our bisimulations between distributions in a pLTS are simply an alternative characterisation of a standard notion of probabilistic bisimulation equivalence, defined between states in a pLTS.     

Model checking agent programming languages

In this paper we describe a verification system for multi-agent programs. This is the first comprehensive approach to the verification of programs developed using programming languages based on the BDI (belief-desire-intention) model of agency. In particular, we have developed a specific layer of abstraction, sitting between the underlying verification system and the agent programming language, that maps the semantics of agent programs into the relevant model-checking framework. Crucially, this abstraction layer is both flexible and extensible; not only can a variety of different agent programming languages be implemented and verified, but even heterogeneous multi-agent programs can be captured semantically. In addition to describing this layer, and the semantic mapping inherent within it, we describe how the underlying model-checker is driven and how agent properties are checked. We also present several examples showing how the system can be used. As this is the first system of its kind, it is relatively slow, so we also indicate further work that needs to be tackled to improve performance.     

Fabrication of polystyrene microfluidic devices using a pulsed CO<Subscript>2</Subscript> laser system

In this article, we described a simple and rapid method for fabrication of droplet microfluidic devices on polystyrene substrate using a CO₂ laser system. The effects of the laser power and the cutting speed on the depth, width and aspect ratio of the microchannels fabricated on polystyrene were investigated. The polystyrene microfluidic channels were encapsulated using a hot press bonding technique. The experimental results showed that both discrete droplets and laminar flows could be obtained in the device.     

Counting clicks and beeps: Exploring numerosity based haptic and audio PIN entry

Haptic and audio cues now appear commonly in computer interfaces, partially due to inherent advantages such as their support for eyes-free interaction. Their invisible, unobservable nature also makes them ideal candidates for security interfaces in which users have to enter secret information such as passwords. In particular, researchers have explored this idea through the design of PIN entry authentication systems based on multi-modal combinations of visual and non-visual content or on the recognition of small sets of unimodal haptic or audio stimuli. This paper highlights the benefits and performance limitations of these approaches and introduces an alternative based on unimodal audio or haptic temporal numerosity – the ability to accurately and rapidly determine the number of cues presented in rapid temporal succession. In essence, in a numerosity interface, rather than recognizing distinct cues, users must count the number of times that a single cue occurs. In an iterative process of design and evaluation, three prototypes implementing this concept are presented and studies of their use reported. The results show the fastest PIN entry times and lowest error rates to be 8 s and 2%, figures that improve substantially on previous research. These results are attained while maintaining low levels of workload and substantial resistance to observation attack (as determined via camera attack security studies). In sum, this paper argues that unimodal audio and haptic numerosity is a valuable and relatively unexplored metaphor for non-visual input and demonstrates the validity of this claim in the demanding task of unobservable authentication systems.